气候变化加速了冰川的大范围退缩，导致冰湖广泛发育。掌握全球冰湖空间分布的整体情况，是监测其溃决灾害的重要基础。本数据采用半自动制图方法并实施严格质量控制，对全球范围内（不含南极和格陵兰冰盖/冰原）面积≥0.01 km2的冰湖进行编目，共识别出 117352 个冰湖，净总面积为 24755.84±2971.33 km2。评估结果表明，该全球冰湖编目（GIGLak）在数量和面积上的总体精度分别为 89.37% 和 91.42%。\n
\n这些冰湖广泛分布于不同海拔区域，从地球第三极延伸至沿海地带。多数冰湖集中分布在格陵兰周边、亚洲高山区、阿拉斯加、加拿大及科迪勒拉山脉地区。面积在 0.01-0.1 平方公里之间的冰湖数量占总数的 77.24%，但其面积占比仅为 11.82%。将冰湖划分为 4 类的结果显示，在全球范围内，非冰接触型冰前湖在数量（占比 67.07%）和面积（占比 53.04%）上均占主导地位。
", "ds_source": "采用 GSW（1984-2020 年，取最大水域范围）和 GLAD（1999-2020 年，聚合年度图层提最大范围）两个开源数据集提取冰湖水域，GLAD 补充 GSW 未识别的狭长冰湖；同时用 MERIT DEM 区分误判为冰湖的地形阴影（生成 > 10° 坡度图辅助非水体判断），借 RGI v6.0 冰川编目确定冰川位置与范围。", "ds_process_way": "
（1）采用 GSW（1984-2020 年，取最大水域范围）和 GLAD（1999-2020 年，聚合图层提最大范围）提取冰湖水域，GLAD 补充 GSW 未识别的狭长冰湖；
\n（2）借 MERIT DEM 区分地形阴影（生成 > 10° 坡度图辅助判非水体），用 RGI v6.0 定冰川范围。
（3）以冰川末端 3 公里缓冲区识别冰湖，叠加高分辨率影像提边界。
（4）人工核查地表水矢量与 ESRI 影像，删伪冰湖、补遗漏，2 人 2020-2022 年耗时 2600 小时处理约 600 万多边形。按融水补给分 GFL 与 NGFL，GFL 依冰川拓扑关系再分 3 类并人工修正。假设人工误差呈高斯分布，仅考虑混合像元影响做精度评估。", "ds_quality": "
数据质量较好。", "ds_acq_start_time": "2022-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": 55.0, "ds_acq_lon_west": null, "ds_acq_lat_north": 83.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 123933413, "ds_files_count": 2, "ds_format": "shp", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "17ccf5cb-1c6a-44a3-8f15-d1395133f623.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": [ "170.45" ], "quality_level": 3, "publish_time": "2025-09-29 21:25:16", "last_updated": "2026-01-12 11:11:23", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "i18n": { "en": { "title": "Global inventory of glacial lakes (GIGLak)", "ds_format": "", "ds_source": "
Two open-source datasets, GSW (1984-2020, taking the maximum water area range) and GLAD (1999-2020, aggregating annual layers to extract the maximum range), were used to extract glacial lake water areas. GLAD supplemented the narrow and elongated glacial lakes not identified by GSW; At the same time, MERIT DEM was used to distinguish terrain shadows that were mistakenly identified as glacial lakes (generating>10 ° slope maps to assist in non water body judgment), and RGI v6.0 glacier cataloging was used to determine the location and range of glaciers.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
Climate change accelerates the extensive retreat of glaciers, leading to the widespread development of glacial lakes. A holistic picture of the spatial distribution of glacial lakes worldwide is a critical base for tracking the outburst hazards. By employing a semi-automated mapping approach and rigorous quality control, this study inventories 117,352 glacial lakes (≥0.01 km2) worldwide (the ice cap/sheet of Antarctic and Greenland excluded), with a net area of 24,755.84 ± 2,971.33 km2. The evaluation result shows this global inventory of glacial lakes (GIGLak) has an overall accuracy of 89.37% and 91.42% in number and area, respectively. These glacial lakes are widely distributed in different altitudes, ranging from the Earth’s third pole to the coasts. Most glacial lakes are distributed in the Greenland periphery, High-Mountain Asia, Alaska, Canada, and the Cordilleras. The number of glacial lakes between 0.01–0.1 km2 accounts for 77.24% of the total count but only 11.82% in area. The classification of glacial lakes as four types indicates that the ice-uncontacted proglacial lakes dominate the number (67.07%) and area (53.04%) worldwide.
", "ds_time_res": "", "ds_acq_place": "global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "(1) Using GSW (1984-2020, taking the maximum water area range) and GLAD (1999-2020, aggregating layers to take the maximum range) to extract glacial lake water areas, GLAD supplements the narrow glacial lakes not identified by GSW;
\n(2) Using MERIT DEM to distinguish terrain shadows (generating>10 ° slope maps to assist in identifying non water bodies), and using RGI v6.0 to determine glacier ranges.
(3) Identify glacial lakes using a 3-kilometer buffer zone at the end of the glacier, and overlay high-resolution images to extract boundaries.
(4) Manually verifying surface water vectors and ESRI images, removing fake glacial lakes, and filling in omissions, two people spent 2600 hours processing approximately 6 million polygons from 2020 to 2022. According to the meltwater supply, GFL and NGFL are classified, and GFL is further divided into three categories based on glacier topology and manually corrected. Assuming that the artificial error follows a Gaussian distribution, only the influence of mixed pixels is considered for accuracy evaluation.", "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", "冰湖", "遥感" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2024 ], "ds_contributors": [ { "true_name": "宋春桥", "email": "cqsong@niglas.ac.cn", "work_for": "中国科学院南京地理与湖泊研究所水安全湖泊与流域科学国家重点实验室", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "宋春桥", "email": "cqsong@niglas.ac.cn", "work_for": "中国科学院南京地理与湖泊研究所水安全湖泊与流域科学国家重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "宋春桥", "email": "cqsong@niglas.ac.cn", "work_for": "中国科学院南京地理与湖泊研究所水安全湖泊与流域科学国家重点实验室", "country": "中国" } ], "category": "水文" }