用于以河流为中心的大样本研究的全球河流特征、水文气象和流域属性综述（GSHA）。根据从网络上获取的排水观测数据，GSHA 涵盖了 13 个机构的 21,568 个流域，时间长达 43 年。GSHA 包括从每日排水观测数据、每日气象变量（包括降水量、2 米气温、长波和短波辐射、风速、实际蒸散量和潜在蒸散量 (AET 和 PET)）中得出的年度溪流特征、每日或每周的蓄水条件（4 层土壤水分、地下水和雪深水当量）、每日植被指数（叶面积指数 （LAI））、每年的 LULC 特征（城市、耕地和森林部分）以及每年的水库信息（调节度 （DOR）和水库容量）。每个气象变量都包含多个独立数据源，以提供不确定性估计。由于其他研究人员也做了类似的工作，因此我们没有额外提取土地地貌、土壤和地质等静态属性，而是通过提供河流 ID 匹配表，直接将我们的测量位置与 HydroATLAS 数据集进行匹配。", "ds_source": "
该数据集涵盖了来自 21 个机构的 568,13 个流域，时间长达 43 年，基于从网络上抓取的排放观测。除年径流指数外，各流域的日气象变量（即降水量、2m气温、长波/短波辐射、风速、实际和潜在蒸散量）、日-周储水量（即雪水当量、土壤水分、地下水百分比）以及地表特征的年度动态描述符（即城市/耕地/森林比例、叶面积指数、水库蓄积量和调节程度）也是通过结合公开可用的遥感和再分析数据集提供。所有气象变量的不确定性都是用独立的数据来源估计的。", "ds_process_way": "
通过结合公开可用的遥感和再分析数据集得到。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": null, "ds_acq_end_time": null, "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": 69168159875, "ds_files_count": 15, "ds_format": "", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "3585e730-8191-4771-bcb7-470c08e58037.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": [ "170.55" ], "quality_level": 3, "publish_time": "2023-12-28 14:45:32", "last_updated": "2025-06-30 16:24:54", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB4097.2023", "i18n": { "en": { "title": "A review dataset of global flow characteristics, hydro meteorological and watershed attributes (GSHA) for large sample studies centered on rivers", "ds_format": "", "ds_source": "
This dataset covers 568,13 watersheds from 21 institutions over a period of 43 years, based on emission observations captured from the internet. In addition to the annual runoff index, the daily meteorological variables (i.e. precipitation, 2m temperature, longwave/shortwave radiation, wind speed, actual and potential evapotranspiration), daily weekly water storage (i.e. snow water equivalent, soil moisture, groundwater percentage), and annual dynamic descriptors of surface features (i.e. urban/cultivated land/forest ratio, leaf area index, reservoir storage and regulation degree) of each watershed are also provided by combining publicly available remote sensing and reanalysis datasets. The uncertainty of all meteorological variables is estimated using independent data sources.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
Global River Characteristics, Hydrometeorology, and Watershed Attributes Review (GSHA) for Large Sample Studies Centered on Rivers. According to drainage observation data obtained from the internet, GSHA covers 21568 watersheds from 13 institutions over a period of 43 years. GSHA includes annual stream characteristics derived from daily drainage observation data, daily meteorological variables (including precipitation, 2-meter temperature, longwave and shortwave radiation, wind speed, actual evapotranspiration, and potential evapotranspiration (AET and PET)), daily or weekly water storage conditions (4-layer soil moisture, groundwater, and snow depth equivalent), daily vegetation index (leaf area index (LAI)), annual LULC characteristics (urban, cultivated land, and forest parts), and annual reservoir information (degree of regulation (DOR) and reservoir capacity). Each meteorological variable contains multiple independent data sources to provide uncertainty estimates. Since other researchers have also done similar work, we did not extract additional static attributes such as land topography, soil, and geology. Instead, we directly matched our measurement locations with the HydroATLAS dataset by providing a river ID matching table.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Obtained by combining publicly available remote sensing and reanalysis datasets.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 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": [ "径流特征", "水文气象", "流域属性 (GSHA)" ], "ds_subject_tags": [ "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [], "ds_contributors": [ { "true_name": "林佩蓉", "email": "peironglinlin@pku.edu.cn", "work_for": "北京大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "林佩蓉", "email": "peironglinlin@pku.edu.cn", "work_for": "北京大学", "country": "中国" } ], "ds_managers": [ { "true_name": "林佩蓉", "email": "peironglinlin@pku.edu.cn", "work_for": "北京大学", "country": "中国" } ], "category": "水文" }