基于植被-土壤-景观建模的高寒草地土壤有机碳主导因素与形成机制研究

Soil organic carbon (SOC) is a key property that not only reveals the underlying SOC processes and maintains water quality but also has profound significance to the global climate system. Much effort has been put into estimating SOC stocks and dynamics over time. However, the uncertainty in modeled SOC stocks employed by researchers hamper the comparability of such works, implying its important role in SOC assessments on large scale and national policy making. Improved predictions of SOC stocks should be possible if the factors determining national, regional and local distributions of SOC are better understood. This proposal takes the alpine grassland of the source region of Yellow river on the Qinghai-Tibet Plateau as the research project. This work is to conduct as part of a broader investigation based on the conditional Latin Hypercube Sampling aimed at quantifying relationships between agricultural intensification, botanical composition and soil properties, including soil microbial biomass carbon in alpine grassland. A holistic vegetation-soil-landscape modeling framework integrating with the Comprehensive Sequential Classification System of Grassland which consists of five components: model conceptualization, data compilation, process identification, parsimonious model calibration, and model validation will be proposed. The aim of the study is to reveal the dominant factors and formation mechanism of the SOC stocks of alpine meadow. The focus of this study is to demonstrate a new holistic vegetation-soil-landscape model based on a comprehensive environmental variable pool using variable selection techniques that serve two purposes revealing the underlying processes and making predictions of SOC. The incorporation of digital soil mapping and modeling into the digital soil model will be to improve soil predictions to simulate the spatial pattern of SOC of alpine grassland at the source region of Yellow river on the Qinghai-Tibet Plateau. Combined with the scenario analysis of future management and environmental changes of the source region of Yellow river, it will reveal the conceivable responses of the SOC stocks of alpine grassland of the source region of Yellow river, and especially the potential increase of SOC stocks to the recovered successions of degraded alpine grassland. The serial achievements of this research will be crucial in the exploration of the grazing management strategy to reduce carbon emission of alpine grassland, increase carbon sink, and extend the preservation of soil carbon. It also has a practical significance to curb ecological degradation, and to ensure the ecological security as well as the sustainable development of the source region of Yellow river on the Qinghai-Tibet Plateau.

土壤有机碳是集环境过程、维持水质和气候变化反映能力于一体的综合性指标,由于估算中存在较大不确定性,成为不同区域土壤有机碳储量比较、大尺度上有机碳储量评估以及国家政策制定的主要障碍。本研究以青藏高原黄河源区高寒草地为对象,以条件超拉丁方取样为基础,基于草地综合顺序分类系统在整体植被-土壤-景观模型框架下,通过模型概念化、数据收集、过程识别、模型构建和模型验证5个步骤,揭示高寒草地土壤有机碳主导因素与形成机制,构建并验证高寒草地土壤有机碳整体植被-土壤-景观模型;开展高寒草地土壤有机碳的数字模拟和制图研究,揭示黄河源区高寒草地土壤有机碳空间分布格局;针对未来情景,阐明黄河源区高寒草地土壤有机碳未来的可能响应以及退化草地恢复的土壤有机碳增碳潜力。项目的系列研究结果对于探讨减少高寒草地碳排放、增加土壤碳贮存、延长土壤碳驻留的放牧调控管理策略,确保黄河源区生态安全和经济可持续发展具有重要的现实意义。

准确估算草地生态系统土壤有机碳储量并了解其空间分布特征和对草地退化恢复演变的响应规律是草地碳循环研究的关键问题。采用条件超拉丁等方法，历时4年通过系统取样、农牧户入户调查获得了黄河源区高寒草地土壤有机碳全部相关变量关键数据库；明确了环境变量对土壤有机碳的影响；获得的基于机器学习算法的黄河源区高寒草地土壤有机碳整体植被-土壤-景观最优模型，实现了黄河源区土壤有机碳含量和碳储量价值的精准估算，也为大尺度范围内数字土壤制图提供了模型借鉴，为研究区农业发展政策建议的制定提供了数据支撑；明确了草地退化关键期的阈值效应、高寒草地土壤有机碳对气候变化的响应机制；项目的系列研究结果对于制定退化草地恢复策略，以及制定减少黄河源区高寒草地碳排放、增加土壤碳贮存、延长土壤碳驻留的放牧调控管理策略,遏制生态环境退化,确保黄河源区生态安全和经济可持续发展具有重要的现实意义。项目在研期间共发表论文19篇，其中SCI论文10篇，7名硕士研究生、3名博士研究生依托本项目完成学位论文。本项目已达到预期目标，在科研产出和人才培养等方面超额完成了任务。