本数据集聚焦青藏高原土壤碳库对气候变化与放牧的响应，旨在厘清二者交互作用下的土壤碳库动态。当前青藏高原土壤碳库被认为面临气候变暖加剧与放牧强度增加的双重威胁，但模型预测的不确定性使其变化趋势尚不明确。数据集整合了大规模土壤调查、土壤培养实验及配对放牧试验数据，基于三库土壤碳模型模拟气候与放牧对土壤碳储量的影响。\n
本数据集为揭示放牧对气候诱导型土壤碳汇的调控机制提供支撑，可服务于青藏高原放牧管理优化及地球系统模型中土壤碳-放牧交互模块的完善。", "ds_source": "
土壤碳数据库：整合 2019-2022 年青藏高原大规模标准化野外调查的 1608 个土壤样本，及过去三十年已发表研究的 2562 个土壤碳数据，共 4170 条观测数据，覆盖 0-300cm 深度，含高山草甸、森林等多种植被类型，包含碳储量、容重、pH 等指标。
土壤培养实验数据：从 WoS、CNKI 检索 “土壤碳”“培养”“青藏高原” 相关文献，筛选 19 项有氧条件下持续半年以上的长期培养实验数据，提取 CO₂排放时序数据。
放牧实验数据：以 “土壤碳”“放牧”“青藏高原” 为关键词检索 WoS、CNKI，筛选 52 篇同行评议研究，获取 296 条分深度配对观测数据，含放牧与禁牧样地的碳储量、植被生物量、土壤质地等信息。
辅助数据集：包括 CMIP6 的 ESMs 气候与 NPP 预测数据、CRU 气候数据、MODIS/GIMMS3g 的 NPP 数据、青藏高原植被分布图、冻土类型等地理环境数据，及中国统计年鉴的牲畜数量数据。", "ds_process_way": "
土壤碳储量模拟：以气候、植被等为变量，用分层随机森林模型构建不同深度碳储量预测模型，经十折交叉验证优化，结合 1km 网格升尺度生成空间分布图，Bootstrap 法量化不确定性。
碳周转时间估算：三库碳分解模型拟合培养实验 CO₂排放数据，得碳库内在周转时间，温度校正后用随机森林模型分析环境影响，再升尺度至全高原。
未来碳动态预测：碳储量与周转时间数据纳入三库模型，优化参数后，基于 SSP2-4.5、SSP5-8.5 情景及 ESMs 数据预测 2060 年碳库变化，叠加热融喀斯特敏感性评估影响。
放牧影响分析：响应比量化放牧对碳储量的作用，随机森林模型识别关键驱动因子；设计两种放牧情景，结合未来 NPP 数据估算碳储量变化，分析与气候的交互作用。", "ds_quality": "
数据质量较好。", "ds_acq_start_time": "2019-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "青藏高原", "ds_acq_lon_east": 104.51666666666667, "ds_acq_lat_south": 26.0, "ds_acq_lon_west": 73.66666666666667, "ds_acq_lat_north": 39.75, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 111444168, "ds_files_count": 2, "ds_format": "tiff", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "46820e1f-6a0e-4822-9288-3c6529120961.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.30" ], "quality_level": 3, "publish_time": "2025-09-29 21:25:10", "last_updated": "2026-01-14 11:01:45", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "i18n": { "en": { "title": "Grazing reverses climate-induced soil carbon gains on the Tibetan Plateau", "ds_format": "tiff", "ds_source": "
Soil Carbon Database: Integrating 1608 soil samples from large-scale standardized field surveys on the Qinghai Tibet Plateau from 2019 to 2022, as well as 2562 soil carbon data from published studies over the past thirty years, with a total of 4170 observation data covering depths of 0-300cm, including multiple vegetation types such as alpine meadows and forests, and indicators such as carbon storage, bulk density, and pH.
Soil cultivation experiment data: Retrieve relevant literature on \"soil carbon\", \"cultivation\", and \"Qinghai Tibet Plateau\" from WoS and CNKI, screen 19 long-term cultivation experiment data under aerobic conditions lasting more than six months, and extract CO ₂ emission time-series data.
Grazing experiment data: Using keywords such as \"soil carbon\", \"grazing\", and \"Qinghai Tibet Plateau\", WoS and CNKI were searched to select 52 peer-reviewed studies, and 296 depth paired observation data were obtained, including carbon storage, vegetation biomass, soil texture, and other information of grazing and prohibited grazing sites.
Auxiliary dataset: including ESMs climate and NPP prediction data from CMIP6, CRU climate data, MODIS/GIMMS3g NPP data, vegetation distribution map of the Qinghai Tibet Plateau, permafrost types and other geographical environment data, as well as livestock population data from the China Statistical Yearbook.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
Soil carbon stocks on the Tibetan Plateau are widely considered to be increasingly threatened by drastic climate warming and intensified livestock grazing. But it remains elusive due to unconstrained model projections. Here we integrate large-scale soil campaigns, soil incubation with paired grazing experiments to project impacts of climate change and grazing on soil carbon stocks in a three-pool soil carbon model. While Tibetan soils will act as a carbon sink, over half of the gains occur in active or unprotected pools, making them vulnerable to extreme events and grazing. Although thermokarst processes may not reverse this trend, continued livestock grazing at current levels, or even a transition to a forage-livestock balanced state, could nearly offset climate-induced benefits. We highlight the critical need to optimize grazing to sustain soil carbon sinks on the Tibetan Plateau, and emphasize the importance of incorporating grazing impacts on soil carbon stocks into Earth system models.
", "ds_time_res": "年", "ds_acq_place": "Qinghai-Tibet Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Soil carbon storage simulation: Using climate, vegetation, and other variables, a hierarchical random forest model is used to construct carbon storage prediction models at different depths. After ten fold cross validation optimization, a spatial distribution map is generated by upscaling a 1km grid, and uncertainty is quantified using Bootstrap method.
Estimation of carbon turnover time: fitting the CO ₂ emission data of the cultivation experiment with a three reservoir carbon decomposition model to obtain the internal turnover time of the carbon reservoir. After temperature correction, using a random forest model to analyze environmental impacts, and then upscaling to the entire plateau.
Future carbon dynamic prediction: Carbon storage and turnover time data are incorporated into the three reservoir model, and after optimizing parameters, carbon storage changes in 2060 are predicted based on SSP2-4.5, SSP5-8.5 scenarios, and ESMs data, combined with thermal karst sensitivity assessment.
Grazing impact analysis: quantifying the effect of grazing on carbon storage using response ratio, identifying key driving factors using a random forest model; Design two grazing scenarios, estimate carbon storage changes based on future NPP data, and analyze their interaction with climate.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "青藏高原", "土壤碳储量", "气候变暖与放牧" ], "ds_subject_tags": [ "地球化学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "ds_time_tags": [ 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "汪涛", "email": "twang@itpcas.ac.cn", "work_for": "中国科学院青藏高原研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "汪涛", "email": "twang@itpcas.ac.cn", "work_for": "中国科学院青藏高原研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "汪涛", "email": "twang@itpcas.ac.cn", "work_for": "中国科学院青藏高原研究所", "country": "中国" } ], "category": "地球化学" }