The global dryland lake storage (GDLS) dataset

Monthly lake areas, surface elevation, and storage changes for 105,400 lakes and reservoirs in global drylands. This dataset contains five files:1. 1_Dryland_lakeArea.parquetMonthly lake areas (square kilometer) for 105400 lakes and reservoirs from Mar 1984 to Dec 2020.2. 2_Dryland_lakeElevation.parquetMonthly lake elevation (m) for 105400 lakes and reservoirs from Mar 1984 to Dec 2020.3. 3_Dryland_lakeStorage.parquetMonthly active lake storage (million cubic meter) for 105400 lakes and reservoirs from Mar 1984 to Dec 2020.4. 4_Dryland_lakeLocation.zipPoint shapefile for 105400 lakes and reservoirs. Derived from the HydroLAKES v1.0 dataset.5. 5_Dryland_lakeUncertainty.csvThe uncertainty for DEM-based hypsometric relationship. Column "source" represents the DEM source. "occurrence_min" represents the minimum occurrence value for a lake. "occurrence_max" represents the maximum occurrence value for a lake. "elevation_min" represents the minimum value of the DEM (typically the water surface elevation of the DEM). "elevation_max" represents the maximum value of the DEM within the lake mask. "occurrence_lowestElev" represents the occurrence value that is corresponding to the DEM water surface. "number_of_occurrences" represents the number of occurrences that are less than 98%. "occurrence_pct" represents the percentage of exposed occurrences over entire occurrence range (occurrence_lowestElev/occurrence_max). "occurrence_sd" represents the standard deviation of occurrence values (smaller value means a more uniform distribution of occurrence).6. displayExample.ipynbA Python example to show the location of a given lake and to plot monthly time series of area/elevation/storage for this lake.Please cite: G. Zhao, H. Gao, Y. Li, Q. Tang, R. I. Woolway, J. Merder, L. Rosa, A. M. Michalak (2025), Decoupling of surface water storage from precipitation in global drylands due to anthropogenic activity, Nature Water