本数据基于1980-2020年黄河流域10公里分辨率的碳库模拟数据，展现了区域有机碳储量的时空动态变化。数据集涵盖了黄河流域32°N–42°N，96°E–119°E的区域，包括青海、四川、甘肃等9个省份的全部或部分地区。通过结合气候数据和土地利用信息，采用干旱生态系统模型（AEM）完成碳库动态模拟。数据经多源验证和质量控制，具有较高的空间精度与可靠性。本数据集可为区域碳循环研究、碳汇评估及气候变化适应性策略提供重要支撑。
", "ds_source": "模拟所需的输入数据包括气象数据（如1980-2020年日均温度、最高温度、最低温度、降水量、相对湿度）和土地利用数据，CO2数据等。气象数据来自ERA5气象再分析数据集，土地利用数据则来源于资源环境科学数据平台，采用了1980年的年度土地利用变化信息。所有输入数据均通过插值处理，重采样至10公里的空间分辨率，并根据地理坐标进行投影转换，以保证不同数据源的空间一致性。
", "ds_process_way": "模型采用干旱生态系统模型（AEM），该模型能够有效模拟干旱地区的碳库动态。根据黄河流域的特点，模型参数设置包括土壤类型、生物量分布、气候变量等区域特定参数。模型运行过程中，输入的气象数据和CO2数据将被逐年加载，模型根据设定的参数进行碳库模拟，并输出每年碳库变化的栅格数据。每年的碳库数据将为后续的分析提供基础，进一步揭示黄河流域碳库变化的趋势和影响因素。
", "ds_quality": "本数据采用多种手段进行质量控制： (1) 数据源验证：对比ERA5和其他再分析数据确保输入气象数据的准确性； (2) 模型验证：通过采样地面观测数据验证模型输出的准确性，均方误差（RMSE）保持在合理范围内。黄河流域主要区域属于中国干旱半干旱区，已在中国干旱半干旱区碳库的数据集中对植被碳、土壤碳进行了验证； (3) 数据一致性检查：确保时间序列数据的连续性和空间分布的逻辑性。
", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "黄河流域", "ds_acq_lon_east": 119.0, "ds_acq_lat_south": 42.0, "ds_acq_lon_west": 96.0, "ds_acq_lat_north": 32.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1319835, "ds_files_count": 5, "ds_format": "grid", "ds_space_res": "10 km×10 km", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "8b10ab0e-22be-4b7c-b718-558f3b86e5b7.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-01-21 11:41:27", "last_updated": "2026-05-07 11:13:51", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6728.2025", "i18n": { "en": { "title": "Yellow River Basin Organic Carbon Database Dataset (1980-2020)", "ds_format": "grid", "ds_source": "The input data required for simulation includes meteorological data (such as daily average temperature, maximum temperature, minimum temperature, precipitation, relative humidity from 1980 to 2020) and land use data, CO2data, etc. The meteorological data comes from the ERA5 meteorological reanalysis dataset, while the land use data comes from the Resource and Environmental Science Data Platform, using annual land use change information from 1980. All input data is interpolated, resampled to a spatial resolution of 10 kilometers, and projected based on geographic coordinates to ensure spatial consistency across different data sources", "ds_quality": "
This data is subject to quality control using multiple methods: (1) Data source validation: comparing ERA5 with other reanalysis data to ensure the accuracy of input meteorological data; (2) Model validation: Verify the accuracy of the model output by sampling ground observation data, and keep the mean square error (RMSE) within a reasonable range. The main areas of the Yellow River Basin belong to the arid and semi-arid regions of China, and vegetation carbon and soil carbon have been validated in the carbon pool dataset of China's arid and semi-arid regions; (3) Data consistency check: Ensure the continuity of time series data and the logical spatial distribution", "ds_ref_way": "", "ds_abstract": "
This data is based on simulated carbon storage data with a resolution of 10 kilometers in the Yellow River Basin from 1980 to 2020, demonstrating the spatiotemporal dynamic changes of regional organic carbon storage. The dataset covers the area of 32 ° N-42 ° N and 96 ° E-119 ° E in the Yellow River Basin, including all or part of nine provinces including Qinghai, Sichuan, and Gansu. By combining climate data and land use information, a dry ecosystem model (AEM) is used to simulate the dynamics of carbon pools. The data has undergone multi-source verification and quality control, and has high spatial accuracy and reliability. This dataset can provide important support for regional carbon cycle research, carbon sink assessment, and climate change adaptation strategies
", "ds_time_res": "", "ds_acq_place": "Yellow River Basin", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The model adopts the Arid Ecosystem Model (AEM), which can effectively simulate the dynamics of carbon pools in arid regions. According to the characteristics of the Yellow River Basin, the model parameter settings include region specific parameters such as soil type, biomass distribution, and climate variables. During the operation of the model, the input meteorological data and CO2data will be loaded year by year. The model simulates the carbon pool based on the set parameters and outputs grid data of annual carbon pool changes. The annual carbon pool data will provide a basis for subsequent analysis, further revealing the trends and influencing factors of carbon pool changes in the Yellow River Basin", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 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": [ "有机碳库", "相对湿度", "短波辐射" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河流域" ], "ds_time_tags": [ 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "方霞", "email": "xiafangxj@163.com", "work_for": "新疆工程学院", "country": "中国" }, { "true_name": "朱士华", "email": "zhu_shihua2021@163.com", "work_for": "江苏省气候中心", "country": "中国" }, { "true_name": "张耀南", "email": "yaonan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "曹良中", "email": "clz152026@163.com", "work_for": "九江学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "方霞", "email": "xiafangxj@163.com", "work_for": "新疆工程学院", "country": "中国" }, { "true_name": "朱士华", "email": "zhu_shihua2021@163.com", "work_for": "江苏省气候中心", "country": "中国" }, { "true_name": "张耀南", "email": "yaonan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "曹良中", "email": "clz152026@163.com", "work_for": "九江学院", "country": "中国" } ], "ds_managers": [ { "true_name": "方霞", "email": "xiafangxj@163.com", "work_for": "新疆工程学院", "country": "中国" } ], "category": "生态" }