%0 Dataset
%T Global REservoir Inventory and Reservoir Sedimentation Dataset (GREI_v1 and GREI_Sed_v1)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/3478e129-dea1-45b6-b98d-e7001f7bec22
%W NCDC
%R 10.12072/ncdc.hydrology.db7452.2026
%A SONG Chunqiao
%A Liu Kai
%A Fan Chenyu
%K reservoirs;sedimentation;storage loss;machine learning;remote sensing
%X This dataset includes the Global REservoir Inventory (GREI_v1) and the global reservoir sedimentation database (GREI_Sed_v1). GREI_v1 records the spatial locations, boundaries and attributes of 555,960 reservoirs worldwide larger than 0.001 km², including reservoir name, area, location, storage capacity and mean depth. The final inventory covers 469,639.9 km² of reservoir surface area; 85,843 reservoirs have verified storage-capacity records totaling 6,851.7 km³. GREI_Sed_v1 estimates annual sedimentation rates and storage loss using 6,133 field-surveyed sedimentation samples and machine-learning models. The dataset improves the representation of small and newly dammed reservoirs and supports research and management in water resources, hydrology, sediment transport, climate adaptation and sustainability planning.For detailed data information, refer to: Liu, K., Fan, C., Song, C. et al. Global patterns of reservoir sedimentation and overlooked risks in small reservoirs. Nature Sustainability (2026). https://doi.org/10.1038/s41893-026-01859-y