Featured Data

Global Lake Area, Climate, and Population Dataset

July 1, 2021

Susanne Grossman-Clarke


Labou, S.G., M.F. Meyer, M.R. Brousil, A.N. Cramer, and B.T. Luff. 2020. Global lake area, climate, and population dataset ver 4. Environmental Data Initiative. https://doi.org/10.6073/pasta/834e2d4e8ee7eb2fa9a5a5b32d759683 (Accessed 2020-06-30).


This featured data package is a great example of re-using and combining datasets to further new science. Existing global datasets were harmonized to create the Global Lake area, Climate, and Population (GLCP) dataset. The harmonized data are presented in a recent article in the journal Scientific Data (Meyer et al. 2020), along with a description of the kind of research questions to which the GLCP can be applied.

An increasing population in conjunction with a changing climate necessitates a detailed understanding of water abundance at multiple spatial and temporal scales. Remote sensing has provided massive data volumes to track fluctuations in water quantity, yet contextualizing water abundance with other local, regional, and global trends remains challenging by often requiring large computational resources to combine multiple data sources into analytically-friendly formats. To bridge this gap and facilitate future freshwater research opportunities, the GLCP dataset was created.

The GLCP is a compilation of lake surface area for 1.42+ million lakes and reservoirs of at least 10 ha in size from 1995 to 2015 with co-located basin-level temperature, precipitation, and population data. The compiled dataset was created with FAIR data principles (Findable, Accessible, Interoperable, Re-usable) in mind and retains unique identifiers from parent datasets to expedite interoperability. The GLCP offers critical data for basic and applied investigations of lake surface area, and water quantity, at local, regional, and global scales.


Meyer, M.F., Labou, S.G., Cramer, A.N. , Brousil, M.R., Luff, B.T. The global lake area, climate, and population dataset. Sci Data 7, 174 (2020). https://doi.org/10.1038/s41597-020-0517-4

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