全球湖泊/水库地表水范围是许多研究的基本输入数据。虽然目前已有一些数据集，但由于数据来源不同、地表水动态变化特征等各种原因，数据集之间存在不完整或空间不一致等问题。在此，基于 HydroLAKES、GRanD 和 OpenStreetMap，制作了一个新的全球湖泊/水库水面范围数据集（GLRSED），其中包含 217 万个湖泊/水库的空间范围和基本属性（如名称、面积、湖泊类型和来源）。通过将 GLRSED 与其他辅助数据进行空间叠加，我们确定了山区湖泊、内流湖泊、水库、冰川湖泊和永久冻土湖泊等。此外，我们还计算了经过每个湖泊的地表水和海洋地形（SWOT）轨道。该数据集可为全球湖泊/水库监测提供基础数据，也可用于研究人类活动和气候变化对湖泊/水库淡水供应的影响等。
", "ds_source": "1、HydroLAKES 数据集由多个数据源合并而成，包括地形图和遥感数据集。它包含 143 万个面积大于 0.1 平方公里的全球湖泊和水库，其属性包括表面积、周长、平均深度和体积等。HydroLAKES 是最全面、最广泛使用的数据集之一。网址为 http://www.hydrosheds.org；\n
2、OSM是基于志愿者收集和更新的地理信息。数据源来自全球定位系统（GPS）等设备和地籍数据、人工对中高分辨率卫星和航空图像进行数字化，具有全球覆盖和可更新的性质。可从以下网址获取： https://www.openstreetmap.org/；\n
3、GRanD 1.3 版包含 7250 条水库及其相关大坝的记录，其属性包括名称、空间坐标、表面积、库容、大坝高度、建造年份、主要用途等，网址为：http://sedac.ciesin.columbia.edu/pfs/grand.html。", "ds_process_way": "
首先，对数据进行预处理，通过从OSM中所有类型的水中提取湖泊和水库对其进行清理，最终处理过的 OSM 包含总共约 85 万个湖泊的数据。\n
对于 HydroLAKES 和 GRanD 的预处理过程是使用 HydroBASINS 进行分区，以便后续处理。然后，在不同流域对 HydroLAKES、OSM 和 GRanD 进行整合处理，并与其他辅助数据和 GRanD 进行空间重叠。使用 HydroBASINS 第 12 级数据集中的 “Endo ”属性来分析内流变湖泊。Endo \"属性变量，即 >0 。\n
使用 SWORD 来识别位于河流上的湖泊。此外，通过将我们的数据集与 SWORD 轨道，我们计算了经过每个湖泊的轨道数。\n
将上述湖泊类型标记为数据集的属性，并通过地理计算计算出每个对象的面积以及岸线属性。同时，保留了 HydroLAKES 和 GRanD 的 ID 属性。在合并两个或多个对象时，只保留第一个对象。\n
上述过程使用 ArcGIS 完成。", "ds_quality": "
数据在开发数据库的过程中，数据差异和记录空白得到了纠正。然而，由于缺少许多湖泊/水库（如中小型湖泊/水库）的信息，该数据库仍不完整。", "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": 5303352124, "ds_files_count": 4, "ds_format": "shp", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "42a4db8e-311a-4eb5-8d25-def2db23fd3b.png", "ds_thumb_from": 0, "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": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-11-28 11:01:22", "last_updated": "2026-01-14 10:54:58", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6645.2024", "i18n": { "en": { "title": "Global Lake/Reservoir Surface Extension Dataset (GLRSED)", "ds_format": "shp", "ds_source": "
1. The HydroLAKES dataset is a combination of multiple data sources, including topographic maps and remote sensing datasets. It contains 1.43 million global lakes and reservoirs with an area greater than 0.1 square kilometers, and its attributes include surface area, perimeter, average depth, and volume. HydroLAKES is one of the most comprehensive and widely used datasets. The website is http://www.hydrosheds.org ；\n
2. OSM is based on geographic information collected and updated by volunteers. The data source comes from devices such as the Global Positioning System (GPS) and cadastral data, as well as manually digitizing medium to high-resolution satellite and aerial images, with global coverage and renewability. You can obtain it from the following website: https://www.openstreetmap.org/ ；\n
3. GRanD 1.3 version contains 7250 records of reservoirs and their related dams, with attributes including name, spatial coordinates, surface area, storage capacity, dam height, construction year, primary use, etc. The website is: http://sedac.ciesin.columbia.edu/pfs/grand.html .", "ds_quality": "
During the process of developing the database, data discrepancies and record gaps were corrected. However, due to the lack of information on many lakes/reservoirs (such as small and medium-sized lakes/reservoirs), the database is still incomplete.", "ds_ref_way": "", "ds_abstract": "
The global surface water range of lakes/reservoirs is a fundamental input data for many studies. Although there are currently some datasets, due to various reasons such as different data sources and dynamic changes in surface water, there are problems such as incompleteness or spatial inconsistency between datasets. Here, based on HydroLAKES, GRanD, and OpenStreetMap, a new global lake/reservoir water surface range dataset (GLRSED) has been created, which includes the spatial range and basic attributes (such as name, area, lake type, and source) of 2.17 million lakes/reservoirs. By spatially overlaying GLRSED with other auxiliary data, we identified mountainous lakes, inflow lakes, reservoirs, glacial lakes, and permafrost lakes. In addition, we also calculated the surface water and ocean topography (SWOT) trajectories passing through each lake. This dataset can provide basic data for global lake/reservoir monitoring and can also be used to study the impact of human activities and climate change on freshwater supply in lakes/reservoirs.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Firstly, the data is preprocessed by extracting lakes and reservoirs from all types of water in the OSM for cleaning. The final processed OSM contains data from approximately 850000 lakes in total.\n
The preprocessing process for HydroLAKES and GRanD involves partitioning using HydroBASINS for subsequent processing. Then, HydroLAKES, OSM, and GRanD were integrated and processed in different watersheds, and spatially overlapped with other auxiliary data and GRanD. Use the \"Endo\" attribute in the 12th level dataset of HydroBASINS to analyze the internal flow of lakes. Endo \"attribute variable, i.e.>0.\n
Use SWORD to identify lakes located on rivers. In addition, by aligning our dataset with the SWORD orbit, we calculated the number of orbits passing through each lake.\n
Mark the above lake types as attributes of the dataset and calculate the area and shoreline attributes of each object through geographic calculations. Meanwhile, the ID attributes of HydroLAKES and GRanD are retained. When merging two or more objects, only the first object is retained.\n
The above process is completed using ArcGIS.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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": [ "湖泊", "水库", "表面范围" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2023 ], "ds_contributors": [ { "true_name": "白冰心", "email": "baibx@buaa.edu.cn", "work_for": "中国海洋大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "白冰心", "email": "baibx@buaa.edu.cn", "work_for": "中国海洋大学", "country": "中国" } ], "ds_managers": [ { "true_name": "白冰心", "email": "baibx@buaa.edu.cn", "work_for": "中国海洋大学", "country": "中国" } ], "category": "水文" }