{ "created": "2025-05-14 09:58:14", "updated": "2026-05-16 05:30:25", "id": "47816c1b-6a87-4e45-9c1b-3871c3cd2f14", "version": 14, "ds_topic": null, "title_cn": "2001-2100年青藏高原土壤侵蚀数据集", "title_en": "Data set of soil erosion in the Qinghai-Tibetan Plateau from 2001 to 2100", "ds_abstract": "

  本数据集基于概念侵蚀模型和多源数据对青藏高原历史时期（2001-2020）和未来（2021-2100）四种共享社会经济路径（SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5）情景下的冻融侵蚀、水蚀和风蚀进行量化评估。本数据集为揭示该区域土壤侵蚀演变规律、开展生态风险评估及制定水土保持对策提供了重要数据支撑与科学依据。\n

  1. 数据集命名\n

  年份.tif\n

  2. 属性信息

  冻融侵蚀（FTE，无量纲）；水蚀（WAE，t/ha）；风蚀（WIE，kg/m2）", "ds_source": "

 1. 地形和土壤特性数据\n

  地形数据来自国家青藏高原科学数据中心 (https://data.tpdc.ac.cn/)； 土壤特性数据来自联合国粮食及农业组织 (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/)。\n

 2. 历史时期（2001-2020）数据\n

  风速、降水、气温、潜在蒸散量和植被覆盖率数据来自国家青藏高原数据中心 (https://data.tpdc.ac.cn/)。\n

 3. 未来时期（2021-2100）数据\n

  从CMIP6中选择了四种情景SSP1-2.6（SSP1-RCP2.6，可持续发展路径）、SSP2-4.5（SSP2-RCP4.5，中间路径）、SSP3-7.0（SSP3-RCP7.0，区域竞争路径）和 SSP5-8.5（SSP5-RCP8.5，传统化石燃料路径）。 风速、降水、气温、潜在蒸散量和植被覆盖率五个变量来自五个分辨率较高的全球气候模式，包括 CESM2、MPI-ESM1-2-HR、IPSL-CM6A-LR、MRI-ESM2-0和 BCC-CSM2-MR。这些数据可从地球系统网格联合体（https://aims2.llnl.gov/search/cmip6） 下载。其它相关数据请参考Wei et al., 2025。", "ds_process_way": "

 1. 冻融侵蚀\n

  参考第一次全国水利普查中水土流失普查所采用的公式，确定青藏高原冻融区的海拔下限；选取坡度、植被盖度、坡向、气温年较差和年降水量共五个因子作为冻融侵蚀的主要影响因素，构建青藏高原冻融侵蚀敏感性评价体系。通过加权加和法计算综合评价指数，指数值越大，表明冻融侵蚀越强烈。\n

 2. 水蚀\n

  基于修正通用土壤流失方程（RUSLE）中的降雨侵蚀力因子、土壤可蚀性因子、地形因子、覆盖与管理因子以及水土保持措施因子，计算区域土壤水蚀量。\n

 3. 风蚀\n

  基于修正风蚀方程（RWEQ）中的气候因子、土壤可蚀性因子、土壤结皮因子、地表粗糙度因子和植被因子，估算区域土壤风蚀量。\n

 4. 精度控制\n

  基于实测137CS数据对水蚀和风蚀模拟结果进行校验，数据质量良好。", "ds_quality": "

  基于实测137CS数据对水蚀和风蚀模拟结果进行校验。模拟值与实测值具有很好的一致性（水蚀：R2 = 0.839，P < 0.001；风蚀：R2

\n

= 0.634，P < 0.001），表明模型模拟结果具有较高的可靠性。

", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2100-12-31 00:00:00", "ds_acq_place": "青藏高原", "ds_acq_lon_east": 105.72, "ds_acq_lat_south": 21.88, "ds_acq_lon_west": 70.39, "ds_acq_lat_north": 43.580000000000005, "ds_acq_alt_low": 82.0, "ds_acq_alt_high": 8405.0, "ds_share_type": "apply-access", "ds_total_size": 71153853128, "ds_files_count": 4114, "ds_format": "tif", "ds_space_res": "500m", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "47816c1b-6a87-4e45-9c1b-3871c3cd2f14.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-05-14 14:42:41", "last_updated": "2025-05-14 15:08:39", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6841.2025", "i18n": { "en": { "title": "Data set of soil erosion in the Qinghai-Tibetan Plateau from 2001 to 2100", "ds_format": "tif", "ds_source": "

 1. Topographic and Soil Characteristics Data\n

  Topographic data were obtained from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/). Soil characteristics data were sourced from the Food and Agriculture Organization of the United Nations (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/).\n

 2. Historical Period (2001–2020) Data\n

  Data on wind speed, precipitation, temperature, potential evapotranspiration, and vegetation cover were obtained from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/).\n

 3. Future Period (2021–2100) Data\n

  Four scenarios from CMIP6 were selected: SSP1-2.6 (SSP1-RCP2.6, sustainable development pathway), SSP2-4.5 (SSP2-RCP4.5, intermediate pathway), SSP3-7.0 (SSP3-RCP7.0, regional rivalry pathway), and SSP5-8.5 (SSP5-RCP8.5, fossil-fueled development pathway).\n

  Data for five variables including wind speed, precipitation, temperature, potential evapotranspiration, and vegetation cover were derived from five high-resolution global climate models: CESM2, MPI-ESM1-2-HR, IPSL-CM6A-LR, MRI-ESM2-0, and BCC-CSM2-MR. These data can be downloaded from the Earth System Grid Federation (https://aims2.llnl.gov/search/cmip6). For other relevant data, please refer to Wei et al., 2025.", "ds_quality": "

   The simulation results for water and wind erosion were validated using measured 137Cs data. A strong agreement was observed between the simulated and measured values (water erosion: R2=0.839, P<0.001; wind erosion: R2=0.634, P<0.001), indicating that the model outputs are highly reliable.", "ds_ref_way": "", "ds_abstract": "

  This data set, based on conceptual erosion models, including the Freeze-Thaw Erosion Equation (FTEE), the Revised Universal Soil Loss Equation (RUSLE), and the Revised Wind Erosion Equation (RWEQ), and integrated multi-source data, provides a quantitative assessment of freeze-thaw erosion, water erosion, and wind erosion across the Qinghai-Tibetan Plateau during the historical period (2001–2020) and under four Shared Socioeconomic Pathway (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) scenarios for the future period (2021–2100). This data set offers essential data support and scientific foundations for understanding the spatiotemporal dynamics of soil erosion in the region, assessing ecological risks, and formulating soil and water conservation strategies.

", "ds_time_res": "年", "ds_acq_place": "Qinghai-Tibetan Plateau", "ds_space_res": "500m", "ds_projection": "", "ds_process_way": "

 1. Freeze–thaw erosion\n

  Based on the formula used in the First National Water Resources Survey for soil erosion assessment, the lower elevation limit of the freeze–thaw zone on the Tibetan Plateau was determined. Five key factors including slope, vegetation cover, aspect, annual temperature range, and annual precipitation were selected as the main drivers of freeze–thaw erosion. A sensitivity assessment system was constructed accordingly. A comprehensive evaluation index was calculated using a weighted summation method, with higher index values indicating more severe freeze–thaw erosion.\n

 2. Water erosion\n

  Water erosion was estimated based on the Revised Universal Soil Loss Equation (RUSLE), incorporating the rainfall erosivity factor, soil erodibility factor, topographic factor, cover-management factor, and support practice factor.\n

 3. Wind erosion\n

  Wind erosion was estimated using the Revised Wind Erosion Equation (RWEQ), which integrates the climatic factor, soil erodibility factor, soil crust factor, surface roughness factor, and vegetative cover factor.\n

 4. Accuracy control\n

  The simulation results of water and wind erosion were validated using measured 137Cs data, demonstrating good data quality.", "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": [ "青藏高原", "冻融侵蚀", "水蚀", "风蚀" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100 ], "ds_contributors": [ { "true_name": "魏培洁", "email": "peijiew@163.com", "work_for": "中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程重点实验室", "country": "中国" }, { "true_name": "杜娟娟", "email": "djjz2001@163.com", "work_for": "中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程重点实验室", "country": "中国" }, { "true_name": "Ali Bahadur", "email": "alibahadur@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程重点实验室", "country": "中国" }, { "true_name": "张昊玥", "email": "zhhaoyue2024@lzu.edu.cn", "work_for": "兰州大学生态学院", "country": "中国" }, { "true_name": "王世金", "email": "wangshijin@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程重点实验室", "country": "中国" }, { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" }, { "true_name": "陈生云", "email": "sychen@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院,冰冻圈科学国家重点实验室,疏勒河源冰冻圈与生态环境综合监测研究站", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈生云", "email": "sychen@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院,冰冻圈科学国家重点实验室,疏勒河源冰冻圈与生态环境综合监测研究站", "country": "中国" }, { "true_name": "魏培洁", "email": "peijiew@163.com", "work_for": "中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "陈生云", "email": "sychen@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院,冰冻圈科学国家重点实验室,疏勒河源冰冻圈与生态环境综合监测研究站", "country": "中国" } ], "category": "其他" }