A spatially constrained remote sensing-based inventory of glacial lakes worldwide.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-03-20 DOI:10.1038/s41597-025-04809-z
Chunqiao Song, Chenyu Fan, Jinsong Ma, Pengfei Zhan, Xinyuan Deng
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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.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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