该数据集为全球逐月地下水储量异常变化（Groundwater Storage Anomaly , GWSA）降尺度数据，空间分辨率为0.008333°（约为1km），时间跨度为2002.4-2020.12，时间分辨率为月。\n
数据发布格式：多波段栅格（TIFF），坐标系为WGS_1984，可以使用ArcGIS、QGIS等地理信息处理软件进行可视化。\n
文件命名格式：每张多波段栅格命名为“GWSA_YYYYMM”的形式（如“GWSA_200204”），表示全球整年的降尺度结果。月尺度信息以波段的形式存储，并按顺序从1～12月排列，共包含12个波段。\n
加工方法：根据德克萨斯大学空间研究中心（CSR）发布的 GRACE/GRACE-FO RL06 产品提取了月尺度陆地水储量异常（TWSA），并使用奇异谱分析（SSA）填补了时间序列空缺；根据NASA 全球陆面数据同化系统2.1版本（GLDAS Noah v2.1）提取了土壤水、雪水当量和冠层水，并转换为了相对于2004-2009年基准期的变化值。根据垂直水量平衡方程得到粗分辨率（0.25°）下的月尺度GWSA计算结果。随后，建立了包括土壤属性、气候、水文、植被动态在内的19个变量构建了1km尺度的全球环境变量数据库，作为建模的预测因子。使用梯度提升树（XGBoost）在不同含水层建立了降尺度模型，得到了1km的GWSA降尺度结果。\n
数据精度：与原始分辨率GWSA数据进行交叉验证后的整体精度R² = 0.9728，RMSE = -2.0966 cm。与全球1518个独立实测井进行交叉验证后的精度为R² = 0.44，RMSE = 0.51 cm yr⁻¹）。\n
应用范围：为全球水资源管理、识别地下水人类开采足迹与气候变化影响等研究提供高时空分辨率的数据支撑。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "2002-04-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -60.0, "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": "apply-access", "ds_total_size": 176388354269, "ds_files_count": 1, "ds_format": "*.tif", "ds_space_res": "1km", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "WGS 1984", "ds_thumbnail": "6e5c2ad4-4abb-4c21-9a2f-1fbc658eb54b.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": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510", "170.55" ], "quality_level": 0, "publish_time": "2026-05-15 16:07:54", "last_updated": "2026-05-15 16:10:05", "protected": false, "protected_to": "2026-11-11 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.hydrology.db7323.2026", "i18n": { "en": { "title": "1km high-resolution monthly-scale GWSA dataset", "ds_format": "*.tif", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
This dataset provides globally downscaled monthly groundwater storage anomaly (Groundwater Storage Anomaly, GWSA) data for global land areas. The dataset has a spatial resolution of 0.008333° (~1 km at the equator), covering the period from April 2002 to December 2020 with a monthly temporal resolution.\r\n
Data format: The dataset is released in multi-band GeoTIFF format with the coordinate reference system of WGS 1984 (EPSG:4326). The data can be visualized and analyzed using geographic information processing software such as ArcGIS and QGIS.\r\n
File naming convention: Each multi-band raster is named in the format of “GWSA_YYYY” (e.g., “GWSA_2002”), representing the downscaled results for the corresponding year. Monthly information is stored as raster bands arranged in chronological order.\r\n
Data production: Monthly terrestrial water storage anomalies (Terrestrial Water Storage Anomaly, TWSA) were derived from the GRACE/GRACE-FO RL06 products released by the Center for Space Research (CSR), University of Texas, and missing values in the time series were reconstructed using Singular Spectrum Analysis (SSA). Soil moisture storage, snow water equivalent, and canopy water storage were extracted from the NASA Global Land Data Assimilation System Version 2.1 (GLDAS Noah v2.1) and converted into anomaly values relative to the 2004–2009 baseline period. Monthly GWSA at the coarse spatial resolution (0.25°) was then calculated based on the vertical water balance equation. Subsequently, 19 variables including topography, soil properties, climate, hydrology, and vegetation dynamics were used as high-resolution (0.008333°) predictors. XGBoost-based downscaling models were independently developed for different aquifer regions to generate the final 0.008333° GWSA dataset.\r\n
Data accuracy: Cross-validation against the original coarse-resolution GWSA data yielded an overall accuracy of R² = 0.97 and RMSE = 2.10 cm. Validation against 1,518 independent groundwater observation wells worldwide achieved an accuracy of R² = 0.44 and RMSE = 0.51 cm/yr.\r\n
Applications: This dataset provides high spatiotemporal resolution data support for studies related to global water resource management, identification of anthropogenic groundwater depletion footprints, and climate change impacts on groundwater systems.", "ds_time_res": "", "ds_acq_place": "global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "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": [ "重力卫星", "地下水储量异常变化", "高分辨率", "XGBoost" ], "ds_subject_tags": [ "水文学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "樊逸飞", "email": "fanyifei25@mails.ucas.ac.cn", "work_for": "中国科学院青海盐湖研究所", "country": "中国" }, { "true_name": "张宸", "email": "zhangchen241@mails.ucas.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": "韩文霞", "email": "wenxia_han@163.com", "work_for": "中国科学院青海盐湖研究所", "country": "中国" }, { "true_name": "车涛", "email": "chetao@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": "盖迎春", "email": "gtw@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "樊逸飞", "email": "fanyifei25@mails.ucas.ac.cn", "work_for": "中国科学院青海盐湖研究所", "country": "中国" }, { "true_name": "张宸", "email": "zhangchen241@mails.ucas.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": "韩文霞", "email": "wenxia_han@163.com", "work_for": "中国科学院青海盐湖研究所", "country": "中国" }, { "true_name": "车涛", "email": "chetao@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": "盖迎春", "email": "gtw@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "樊逸飞", "email": "fanyifei25@mails.ucas.ac.cn", "work_for": "中国科学院青海盐湖研究所", "country": "中国" }, { "true_name": "张宸", "email": "zhangchen241@mails.ucas.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "水文" }