本数据是基于Global-AI_PET_v3提取的中国新疆范围和中亚五国范围的干旱指数（AI)和蒸散量数据，地理空间数据集以 GeoTIFF（.tif）格式在线提供，使用地理坐标；基准面和椭球体为 WGS84；空间单位为十进制度。空间分辨率为 30 弧秒或 0.008333 度（赤道处约 1 公里）。干旱指数 (Global-AI) 地理数据集已乘以 10,000 倍，以整数形式（保留 4 位小数精度）导出和分发数据。GeoTIFF (.tif) 文件中的 AI 值需要乘以 0.0001 才能获取正确的单位数值。使用 Penman-Monteith 方法计算潜在蒸散量，年平均降水量（MA_Prec）数据来自 WorldClim v 2.1 58 ，该数据为 1970-2000 年期间的平均值。
", "ds_source": "“全球干旱指数和潜在蒸散量数据库-版本 3”（Global-AI_PET_v3）基于 FAO Penman-Monteith 参考蒸散量（ET 0 ）方程，提供高分辨率（30 弧秒）全球水文气候数据，按月和年进行平均（1970-2000 年）。文档概述了实现 Penman-Monteith 方程地理空间的方法，并对结果进行了技术评估。为进行技术验证，将结果与 FAO“CLIMWAT 2.0 for CROPWAT”（ET 0 ：r 2 = 0.85；AI：r 2 = 0.90）的气象站数据以及英国“气候研究单位：时间序列 v 4.04”（ET 0 ：r 2 = 0.89；AI：r 2 = 0.83）的气象站数据进行了比较，但与数据库的早期版本存在显著差异。当前版本的 Global-AI_PET_v3取代了之前的版本，与真实世界气象站数据的相关性更高。该数据库采用普遍认可的参考 ET0估计标准方法开发，连同随附的源代码，为在快速变化的气候条件下进行各种科学应用提供了可靠的工具。
", "ds_process_way": "联合国环境规划署提供的干旱指数分类方案：干旱指数值（Aridity Index Value）<0.03 对应的气候类型是极端干旱，0.03–0.2干旱，0.2–0.5半干旱，0.5–0.65干燥亚湿润，>0.65 湿润。\n\n数据文件命名：XJ-新疆，KAZ-哈萨克斯坦，KGZ-吉尔吉斯斯坦，TJK-塔吉克斯坦，UZB-乌兹别克斯坦，TKM-土库曼斯坦，Global-全球。\n关键缩写解释：AI-干旱指数值（Aridity Index Value），PET-潜在蒸散发（Potential Evapotranspiration）
", "ds_quality": "根据数据作者在数据论文的描述：经过技术评估确认，采用标准化的FAO-56 Penman-Monteith方法估算参考蒸散量（ET₀）、基于地理空间算法生成的Global-AI_PET_v3数据集，提供了覆盖全球（分辨率为30弧秒）的潜在蒸散量（PET）与干旱指数（AI）估算。该数据集适用于地方、国家、区域乃至全球多种尺度的非关键任务应用。尽管已知地方地形、景观异质性及稀疏气象站网络区域的插值过程会增加地块/田地尺度级别的不确定性，评估结果表明：当前版本相较于过往版本性能显著提升，且与实测气象站数据展现出强相关性。综合技术评估结论，Global-AI_PET_v3数据集（含源代码）被评估为一项宝贵的全球性公共科学资源，在全球范围作为参考基准具有比较优势，是气候快速变化背景下各类科学研究的可靠工具。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 7345303812, "ds_files_count": 821, "ds_format": "", "ds_space_res": "1km", "ds_time_res": "无", "ds_coordinate": "WGS84", "ds_projection": "GCS_WGS84", "ds_thumbnail": "4ee287de-f07e-46b8-bc6a-2994576b9957.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "Zomer, R.J., Xu, J. & Trabucco, A. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Sci Data 9, 409 (2022). https://doi.org/10.1038/s41597-022-01493-1", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 1, "publish_time": "2025-08-07 18:11:06", "first_publish_time": null, "last_updated": "2025-08-07 18:11:06", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "license": null, "extra": null, "files_shape": [ { "name": "Global-AI_PET_v3 - Readme.pdf", "size": 887130, "is_dir": false }, { "name": "GlobalPET.jpg", "size": 272830, "is_dir": false }, { "name": "s41597-022-01493-1.pdf", "size": 7974139, "is_dir": false }, { "name": "Global-AI_ET0_annual_v3", "size": null, "is_dir": true }, { "name": "Global-AI_monthly_v3", "size": null, "is_dir": true }, { "name": "Global-ET0_monthly_v3", "size": null, "is_dir": true }, { "name": "KAZ-AI_ET0_v3_annual", "size": null, "is_dir": true }, { "name": "KAZ-AI_v3_monthly", "size": null, "is_dir": true }, { "name": "KAZ-ET0_v3_monthly", "size": null, "is_dir": true }, { "name": "KGZ-AI_ET0_v3_annual", "size": null, "is_dir": true }, { "name": "KGZ-AI_v3_monthly", "size": null, "is_dir": true }, { "name": "KGZ-ET0_v3_monthly", "size": null, "is_dir": true }, { "name": "TJK-AI_ET0_v3_annual", "size": null, "is_dir": true }, { "name": "TJK-AI_v3_monthly", "size": null, "is_dir": true }, { "name": "TJK-ET0_v3_monthly", "size": null, "is_dir": true }, { "name": "TKM-AI_ET0_v3_annual", "size": null, "is_dir": true }, { "name": "TKM-AI_v3_monthly", "size": null, "is_dir": true }, { "name": "TKM-ET0_v3_monthly", "size": null, "is_dir": true }, { "name": "XJ-AI_ET0_v3_annual", "size": null, "is_dir": true }, { "name": "XJ-AI_v3_monthly", "size": null, "is_dir": true }, { "name": "XJ-ET0_v3_monthly", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Drought index and potential evapotranspiration data across Xinjiang and five Central Asian countries (Global-AI_PET_v3)", "ds_abstract": "This data is drought index (AI) and evapotranspiration data extracted based on Global-AI_PET_v3 for Xinjiang in China and five Central Asian countries. Geospatial data sets are provided online in GeoTIFF (.tif) format, using geographical coordinates; the datum and ellipsoid are WGS84; and the spatial unit is decimal degrees. The spatial resolution is 30 arcseconds or 0.008333 degrees (approximately 1 kilometer at the equator). The Drought Index (Global-AI) geographical dataset has been multiplied by a factor of 10,000 to export and distribute data in integer form (retaining 4 decimal places precision). AI values in GeoTIFF (.tif) files need to be multiplied by 0.0001 to obtain the correct unit value. Potential evapotranspiration was calculated using the Penman-Monteith method, and annual average precipitation (MA_Prec) data were obtained from WorldClim v 2.1 58, which is an average for the period 1970-2000.
", "ds_source": "The Global Drought Index and Potential Evapotranspiration Database-Version 3 (Global-AI_PET_v3) provides high-resolution (30 arc-seconds) global hydroclimatic data based on the FAO Penman-Monteith Reference Evapotranspiration (ET0) equation, averaged monthly and annual (1970-2000). The document outlines methods for implementing the Penman-Monteith equation geospatial and provides a technical evaluation of the results. For technical verification, the results were compared with weather station data from the FAO \"CLIMWAT 2.0 for CROPWAT\"(ET0: r2 = 0.85;AI: r2 = 0.90) and weather station data from the UK \"Climate Research Unit: Time Series v4.04\"(ET0: r2 = 0.89;AI: r2 = 0.83), but there were significant differences with earlier versions of the database. The current version of Global-AI_PET_v3 replaces the previous version and is more correlated with real-world weather station data. Developed using the universally recognized standard method of reference ET0 estimation, the database, together with the accompanying source code, provides a reliable tool for various scientific applications in rapidly changing climate conditions.
", "ds_process_way": "The drought index classification scheme provided by the United Nations Environment Program: The climate types corresponding to an Aidity Index Value of 0.03 - 0.2 drought, 0.2 - 0.5 semi-arid, 0.5 - 0.65 dry and sub-humid, and 0.65 humid.\n\nData file names: XJ-Xinjiang, KAZ-Kazakhstan, KGZ-Kyrgyzstan, TJK-Tajikistan, UZB-Uzbekistan, TKM-Turkmenistan, Global-Global.\nExplanation of key abbreviations: AI-Aidity Index Value, PET-Potential Evapotranspiration
", "ds_quality": "According to the data author's description in the data paper: After technical evaluation and confirmation, the standardized FAO-56 Penman-Monteith method was used to estimate the reference evapotranspiration (ET), and the Global-AI_PET_v3 dataset generated based on geospatial algorithms provided estimates of potential evapotranspiration (PET) and drought index (AI) covering the world (resolution of 30 arcseconds). This dataset is suitable for non-critical mission applications at multiple scales at local, national, regional and even global. Although it is known that local topography, landscape heterogeneity, and interpolation processes in sparse weather station network areas increase uncertainty at the plot/field scale level, the evaluation results show that the current version has significantly improved performance compared to previous versions, and has a strong correlation with measured weather station data. According to the comprehensive technical assessment conclusion, the Global-AI_PET_v3 dataset (including source code) has been assessed as a valuable global public scientific resource. It has comparative advantages as a reference benchmark on a global scale and is a reliable tool for scientific research in the context of rapid climate change.
", "ds_ref_instruction": "Zomer, R.J., Xu, J. & Trabucco, A. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Sci Data 9, 409 (2022). https://doi.org/10.1038/s41597-022-01493-1", "ds_projection": "GCS_WGS84", "ds_space_res": "1km", "ds_time_res": "no" } }, "license_type": null, "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": [ "指数", "干旱", "蒸散发" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "乌兹别克斯坦", "哈萨克斯坦", "吉尔吉斯斯坦", "新疆", "塔吉克斯坦", "土库曼斯坦" ], "ds_time_tags": [ 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000 ], "ds_contributors": [ "李锦" ], "ds_meta_authors": [ "李锦" ], "ds_managers": [ "李锦" ], "category": "水文" }