Global lake evaporation volume (GLEV) dataset

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <strong>For an interactive interface of the dataset (Google Earth Engine App), please see https://zeternity.users.earthengine.app/view/glev</strong> -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- There are three csv files in this dataset. Each file has 409 columns and 1427687 rows. <strong>1. 0_evaporation_rate.csv</strong><br> The first column is Hylak_id from HydroLAKES v1.0 dataset.<br> The rest 408 columns contain monthly evaporation rate (mm per day) from Jan 1985 to Dec 2018.<br> <strong>2. 1_openwater_area.csv</strong><br> The first column is Hylak_id.<br> The rest 408 columns contain monthly open water area (square meters) from Jan 1985 to Dec 2018.<br> <strong>Note </strong>that this is not the surface area of lake as shown in the above GEE App.<br> It is the open water area by removing the lake ice coverage.<br> The surface area dataset is available here.<br> <strong>3. 2_evaporation_volume.csv</strong><br> The first column is Hylak_id.<br> The rest 408 columns contain monthly evaporation volume (thousand cubic meter per month) from Jan 1985 to Dec 2018. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <strong>To use this dataset, citation of the following paper is recommended:</strong><br> Zhao, G., Li, Y., Zhou, L., Gao, H. (2022) Evaporative water loss of 1.42 million global lakes. <em>Nature Communications</em>. https://doi.org/10.1038/s41467-022-31125-6 The detailed algorithms associated with the development of GLEV can be found in:<br> Zhao, G., and H. Gao (2019), Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches, <em>Remote Sensing of Environment</em>, 226, 109-124. https://doi.org/10.1016/j.rse.2019.03.015<br> Zhao, G., and H. Gao (2018), Automatic correction of contaminated images for assessment of reservoir surface area dynamics. <em>Geophysical Research Letters</em>, 45, 6092-6099. https://doi.org/10.1029/2018GL078343