Qi Zhang’s research while affiliated with Beijing Normal University and other places

Background and aims Comprehending the mechanisms of soil organic carbon (SOC) accumulation is essential for maintaining soil fertility and combating climate change. However, the potential processes and roles of plant life cycle traits in regulating SOC accumulation over broad geographic scales remain unclear. Methods We generated a map of annual plant prevalence using occurrence/absence records of 4,837 vascular species, integrated with species distribution models. Based on 51 field observations across the Qinghai–Tibetan Plateau (QTP) and a structural equation model, we systematically investigated the direct effects of climate and annual plant prevalence on SOC versus the indirect effects mediated by root turnover and productivity. Results We found that annual plants accounted for only 8.9% of plant species on the QTP. The proportion of annual plants increases with higher temperatures and lower precipitation, indicating that annual plants are more competitive than perennials in arid environments. Furthermore, annual plant prevalence exerted both direct and indirect positive effects on SOC, with indirect effects mediated by changes in belowground net primary productivity, belowground biomass carbon, and root turnover time. Importantly, the higher annual plant prevalence can offset the negative impact of warming on SOC storage. Conclusion Our findings indicate that maintaining a high annual plant prevalence would enhance soil carbon storage and may help offset carbon losses due to global warming. The findings underscore the importance of adequately managing the vegetation of fragile ecosystems like those of the QTP for enhancing soil C sequestration, thereby contributing to climate change mitigation.

Water use efficiency (WUE) reflects the quantitative relationship between vegetation gross primary productivity (GPP) and surface evapotranspiration (ET), serving as a crucial indicator for assessing the coupling of carbon and water cycles in ecosystems. As a sensitive region to climate change, the Qinghai Tibetan Plateau’s WUE dynamics are of significant scientific interest for understanding carbon water interactions and forecasting future climate trends. However, due to the scarcity of observational data and the unique environmental conditions of the plateau, existing studies show substantial errors in GPP simulation accuracy and considerable discrepancies in ET outputs from different models, leading to uncertainties in current WUE estimates. This study addresses these gaps by first employing a machine learning approach (random forest) to integrate observed GPP flux data with multi-source environmental information, developing a predictive model capable of accurately simulating GPP in the Qinghai Tibetan Plateau (QTP). The accuracy of the random forest simulation results, RF_GPP (R² = 0.611, RMSE = 69.162 gC·m⁻²·month⁻¹), is higher than that of the multiple linear regression model, regGPP (R² = 0.429, RMSE = 86.578 gC·m⁻²·month⁻¹), and significantly better than the accuracy of the GLASS product, GLASS_GPP (R² = 0.360, RMSE = 91.764 gC·m⁻²·month⁻¹). Subsequently, based on observed ET flux data, we quantitatively evaluate ET products from various models and construct a multiple regression model that integrates these products. The accuracy of REG_ET, obtained by integrating five ET products using a multiple linear regression model (R² = 0.601, RMSE = 21.04 mm·month⁻¹), is higher than that of the product derived through mean processing, MEAN_ET (R² = 0.591, RMSE = 25.641 mm·month⁻¹). Finally, using the optimized GPP and ET data, we calculate the WUE during the growing season from 1982 to 2018 and analyze its spatiotemporal evolution. In this study, GPP and ET were optimized based on flux observation data, thereby enhancing the estimation accuracy of WUE. On this basis, the interannual variation of WUE was analyzed, providing a data foundation for studying carbon water coupling in QTP ecosystems and supporting the formulation of policies for ecological construction and water resource management in the future.

China has implemented a series of ecological engineering projects to help achieve the 2060 carbon neutrality target. However, the lack of quantitative research on ecological engineering and the contribution of climate change to terrestrial carbon sinks limits this goal. This study uses robust statistical models combined with multiple terrestrial biosphere models to quantify the impact of China's ecological engineering on terrestrial ecosystem carbon sink trends and their differences according to the difference between reality and nonpractice assumptions. The main conclusions include the following: (1) since 1901, 84% of terrestrial ecosystem carbon sinks in China have shown an increasing trend, and approximately 45% of regional carbon sinks have increased by more than 0.1 g C/m2 every 10 years. (2) Considering the impact of human activities and the implementation of ecological engineering in China, approximately 56% of carbon sinks have improved, and approximately 10% of the regions whose carbon sink growth exceeds 50 g C m−2 yr−1 are mainly in the southeast coastal of China. (3) The carbon sequestration potential and effect of the Sanjiangyuan ecological protection and construction project are better than others, at 1.26 g C m−2 yr−1 and 14.13%, respectively. The Beijing–Tianjin sandstorm source comprehensive control project helps alleviate the reduction in carbon sinks, while the southwest karst rocky desertification comprehensive control project may aggravate the reduction in carbon sinks. This study clarifies the potential of China's different ecological engineering to increase carbon sink potential, and distinguishes and quantifies the contribution of climate and human activity factors to it, which is of great significance to the system management optimization scheme of terrestrial ecosystems and can effectively serve the national carbon neutral strategy.