Carbon turnover times shape topsoil carbon difference between Tibetan Plateau and Arctic tundra

Modeling the carbon dynamics of ecosystem in a typical permafrost area

Climate change poses mounting threats to fragile alpine ecosystem worldwide. Quantifying changes in carbon stocks in response to the shifting climate was important for developing climate change mitigation and adaptation strategies. This study utilized a process-based land model (Community Land Model 5.0) to analyze spatiotemporal variations in vegetation carbon stock (VCS) and soil organic carbon stock (SOCS) across a typical permafrost area - Qinghai Province, China, from 2000 to 2018. Multiple potential factors influencing carbon stocks dynamics were analyzed, including climate, vegetation, soil hydrothermal status, and soil properties. The results indicated that provincial vegetation carbon storage was 0.22 PgC (0.32 kg/m2) and soil organic carbon pool was 9.12 PgC (13.03 kg/m2). VCS showed a mild increase while SOCS exhibited fluctuating uptrends during this period. Higher carbon stocks were observed in forest (21.74 kg/m2) and alpine meadow (18.08 kg/m2) compared to alpine steppes (9.63 kg/m2). Over 90 % of the carbon was stored in the 0-30 cm topsoil layer. The contribution rates of soil carbon in the 30-60 cm and 60-100 cm soil layers were significantly small, despite increasing stocks across all depths. Solar radiation, temperature, and NDVI emerged as primary influential factors for overall carbon stocks, exhibiting noticeable spatial variability. For SOCS at different depths, the normalized differential vegetation index (NDVI) was the foremost predictor of landscape-level carbon distributions, which explained 52.8 % of SOCS variability in shallow layers (0-30 cm) but dropped to just 12.97 % at the depth of 30-60 cm. However, the dominance of NDVI diminished along the soil depth gradients, superseded by radiation and precipitation. Additionally, with an increase in soil depth, the influence of inherent soil properties also increased. This simulation provided crucial insights for landscape-scale carbon responses to climate change, and offered valuable reference for other climate change-sensitive areas in terms of ecosystem carbon management.

Multilevel ecological compensation policy design based on ecosystem service flow: A case study of carbon sequestration services in the Qinghai-Tibet Plateau

Ecological compensation is an effective means to reconcile the imbalance of eco-social development between regions and promote enthusiasm for ecological environmental protection. There is some conformity between the theory of ecosystem service flow and ecological compensation, which provides new technical support for the formulation of ecological compensation policy. This study took the Qinghai-Tibet Plateau as the research area, adopted the breaking point model to obtain the spatial characteristics of carbon sequestration flow, and formulated a multilevel ecological compensation policy with Tibet as the design object. The results showed that most of the Qinghai-Tibet Plateau has a carbon sequestration surplus; the central and eastern Qinghai-Tibet Plateau, western Sichuan are successively carbon sequestration supply areas; the Chengdu Plain and Xinjiang were listed as carbon sequestration benefit areas; and the carbon sequestration tended to flow more closely between supply and benefit areas in proximity to each other. Nyingchi, Chamdo, Naqu and Shannan in Tibet need to receive a total ecological compensation of 393.21 million USD, of which 93.71 % is from the national level, 6.02 % is from carbon sequestration benefit areas in other provinces; furthermore, Lhasa and Shigatse in Tibet need to provide the remaining ecological compensation. This study offers innovations for the formulation of ecological compensation policies and provide a new theory for ecological environment management.

Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China

Soil organic carbon (SOC) changes affect the land carbon cycle and are also closely related to climate change. Visible-near infrared spectroscopy (Vis-NIRS) has proven to be an effective tool in predicting soil properties. Spectral transformations are necessary to reduce noise and ensemble learning methods can improve the estimation accuracy of SOC. Yet, it is still unclear which is the optimal ensemble learning method exploiting the results of spectral transformations to accurately simulate SOC content changes in the Three-Rivers Source Region of China. In this study, 272 soil samples were collected and used to build the Vis-NIRS simulation models for SOC content. The ensemble learning was conducted by the building of stack models. Sixteen combinations were produced by eight spectral transformations (S-G, LR, MSC, CR, FD, LRFD, MSCFD and CRFD) and two machine learning models of RF and XGBoost. Then, the prediction results of these 16 combinations were used to build the first-step stack models (Stack1, Stack2, Stack3). The next-step stack models (Stack4, Stack5, Stack6) were then made after the input variables were optimized based on the threshold of the feature importance of the first-step stack models (importance > 0.05). The results in this study showed that the stack models method obtained higher accuracy than the single model and transformations method. Among the six stack models, Stack 6 (5 selected combinations + XGBoost) showed the best simulation performance (RMSE = 7.3511, R2 = 0.8963, and RPD = 3.0139, RPIQ = 3.339), and obtained higher accuracy than Stack3 (16 combinations + XGBoost). Overall, our results suggested that the ensemble learning of spectral transformations and simulation models can improve the estimation accuracy of the SOC content. This study can provide useful suggestions for the high-precision estimation of SOC in the alpine ecosystem.

Plant input does not exert stronger control on topsoil carbon persistence than climate in alpine grasslands

Chen et al. (Ecology Letters, 2021, 24, 1018) concluded that plant input governs topsoil carbon persistence in alpine grasslands. We demonstrated that the excluded direct effect of precipitation on topsoil Δ¹⁴ C in their analysis was significant and strong. Our results provide an alternative viewpoint on the drivers of soil carbon turnover.