本数据集是 1948 年至 2010 年空间分辨率为 0.25°的全球月度干旱数据集，该数据集基于多类型、多尺度干旱指数，即包含雪动力学的标准化湿度异常指数（SZIsnow），由先进数据同化系统的系统场驱动。拟议的 SZIsnow 数据集包括不同的物理水能过程，尤其是雪过程。我们对该数据集的评估表明，它能够区分不同时间尺度的不同干旱类型。我们的评估还表明，该数据集能够充分捕捉不同空间尺度上的干旱。对积雪过程的考虑提高了 SZIsnow 的能力，尤其是在积雪覆盖的高纬度地区（如北极地区）和高海拔地区（如青藏高原）。我们发现，1948 年至 2010 年间，地球陆地面积的 59.66% 呈干燥趋势，其余 40.34% 呈湿润趋势。我们的结果还表明，SZIsnow 数据集可用于捕捉世界各地发生的大规模干旱事件。我们的分析表明，在研究期间，全球发生了 525 次面积大于 500 000 平方公里的干旱事件，其中 68.38% 的持续时间超过 6 个月。因此，这个新的干旱数据集非常适合监测、评估和描述干旱，可作为未来干旱研究的宝贵资源。
", "ds_source": "由于观测数据有限，来自数值模式的水文气象变量通常被用作计算全球尺度干旱指数的源数据（Sawada 和 Koike，2016 年）。因此，在本研究中，全球陆地数据同化系统（GLDAS）提供了计算全球 SZIsnow 的变量。
", "ds_process_way": "1、推导 SZIsnow
\n（1） SZIsnow 的物理表示及其推导；
\n（2）水文核算；
\n（3）符合现有条件的气候系数和降水量 (CAFEC)；
\n2、 湿度异常标准化
\n3、SZIsnow 评估指标
\n4、在空间和时间上识别大规模干旱事件", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "1948-01-01 00:00:00", "ds_acq_end_time": "2010-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 14002527513, "ds_files_count": 8, "ds_format": "NetCDF", "ds_space_res": " 0.25°", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "be8b918e-7c56-4169-8a81-f98ee47b9930.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-06-21 10:25:28", "last_updated": "2026-01-14 11:03:29", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6529.2024", "i18n": { "en": { "title": "Standardized Water Anomaly Index (SZISnow) Global Dataset Including Snow Dynamics (1948-2010)", "ds_format": "NetCDF", "ds_source": "
Due to limited observational data, hydro meteorological variables from numerical models are often used as source data for calculating global scale drought indices (Sawada and Koike, 2016). Therefore, in this study, the Global Land Data Assimilation System (GLDAS) provided variables for calculating global SZIsnow.
", "ds_quality": "The data quality is good.", "ds_ref_way": "", "ds_abstract": "
This dataset is a global monthly drought data set with a spatial resolution of 0.25 ° from 1948 to 2010. It is based on a multi type, multi-scale drought index, namely the Standardized Humidity Anomaly Index (SZIsnow) that includes snow dynamics, and is driven by the system field of advanced data assimilation systems. The proposed SZIsnow dataset includes different physical water energy processes, especially snow processes. Our evaluation of this dataset indicates that it can distinguish different drought types at different time scales. Our evaluation also indicates that the dataset is capable of fully capturing drought at different spatial scales. The consideration of snow accumulation processes has improved the ability of SZIsnow, especially in high latitude areas covered by snow (such as the Arctic region) and high-altitude areas (such as the Qinghai Tibet Plateau). We found that between 1948 and 2010, 59.66% of the Earth's land area showed a dry trend, while the remaining 40.34% showed a wet trend. Our results also indicate that the SZIsnow dataset can be used to capture large-scale drought events occurring around the world. Our analysis shows that during the study period, there were 525 drought events with an area greater than 500000 square kilometers worldwide, of which 68.38% lasted for more than 6 months. Therefore, this new drought dataset is highly suitable for monitoring, evaluating, and describing drought, and can serve as a valuable resource for future drought research.
", "ds_time_res": "月", "ds_acq_place": "Global", "ds_space_res": " 0.25°", "ds_projection": "", "ds_process_way": "\n1、 Derive SZISnow
\n\n(1) The physical representation and derivation of SZISnow
(2) Hydrological accounting
(3) Climate coefficient and precipitation (CAFEC) that meet existing conditions
2、 Normalization of Abnormal Humidity
3. SZISnow evaluation indicators
4、 Identifying large-scale drought events in space and time
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "全球干旱数据集", "标准化水分异常指数", "干旱指数" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 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, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010 ], "ds_contributors": [ { "true_name": "田磊", "email": "tianlei@lzu.edu.cn", "work_for": "兰州大学兰州大学黄河流域绿色发展研究院", "country": "中国" }, { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "田磊", "email": "tianlei@lzu.edu.cn", "work_for": "兰州大学兰州大学黄河流域绿色发展研究院", "country": "中国" }, { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "田磊", "email": "tianlei@lzu.edu.cn", "work_for": "兰州大学兰州大学黄河流域绿色发展研究院", "country": "中国" }, { "true_name": "张宝庆", "email": "baoqzhang@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "生态" }