A Global Lakes/Reservoirs Surface Extent Dataset (GLRSED): An Integration of Multi-Source Data

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Bingxin Bai, Lixia Mu, Yumin Tan
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引用次数: 0

Abstract

The surface water extent of global lakes/reservoirs is a fundamental input data for many studies. Although some datasets are currently available, issues such as incomplete data or spatial inconsistencies persist. In this study, a new Global Lakes/Reservoirs Surface Extent Dataset (GLRSED), which provides a more comprehensive spatial extent and basic attributes (e.g., name, area, source, depth and type) of 2.17 million individual features, was developed based on HydroLAKES and OpenStreetMap (OSM). In addition, by spatially overlaying with mountainous polygon, lakes/reservoirs in mountainous areas were identified. The Global Reservoir and Dam database (GRanD), GlObal geOreferenced Database of Dams (GOODD), Georeferenced global Dams and Reserves (GeoDAR) dataset, and OSM were used to distinguish reservoirs from natural lakes. The lakes/reservoirs in the rivers were identified by overlaying them with the Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD). Similarly, endorheic, glacier-fed and permafrost-fed lakes/reservoirs were identified using the same method. Furthermore, the coverage of the SWOT ground track for each lake/reservoir in the GLRSED was calculated to explore the potential of SWOT in monitoring water resources. Although preliminary and with some limitations, this dataset is promising. It can provide essential data for monitoring global lakes/reservoirs, support refined water resource management, and facilitate comprehensive studies on the impacts of human activities and climate change on these water bodies.

Abstract Image

全球湖泊/水库地表面积数据集(GLRSED):多源数据的集成
全球湖泊/水库的地表水范围是许多研究的基本输入数据。虽然目前有一些数据集可用,但数据不完整或空间不一致等问题仍然存在。基于HydroLAKES和OpenStreetMap (OSM),构建了一个新的全球湖泊/水库地表范围数据集(GLRSED),该数据集提供了更全面的空间范围和217万个个体特征的基本属性(如名称、面积、来源、深度和类型)。此外,通过与山地多边形的空间叠加,对山区湖泊/水库进行了识别。利用全球水库和水坝数据库(GRanD)、全球水坝地理参考数据库(GOODD)、地理参考全球水坝和储量(GeoDAR)数据集和OSM来区分水库和自然湖泊。河流中的湖泊/水库通过与地表水和海洋地形(SWOT) Mission River数据库(SWORD)叠加来确定。同样,利用相同的方法确定了内河、冰川补给和永久冻土补给的湖泊/水库。此外,还计算了GLRSED中各湖泊/水库的SWOT地面轨道覆盖率,探索SWOT在水资源监测中的潜力。虽然这个数据集是初步的,并且有一些限制,但它是有希望的。它可以为监测全球湖泊/水库提供必要的数据,支持精炼水资源管理,促进人类活动和气候变化对这些水体的影响的综合研究。
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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
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