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Global Drainage Density for MERIT-Basins with Machine Learning

Global Drainage Density

A machine learning approach to estimating drainage density (Dd ) based on the watershed-level climate, topography, vegetation, soil, and hydrology conditions globally. Using a high-quality hydrography dataset for the United States, i.e., the medium-resolution National Hydrography Dataset Plus (NHDPlusV2), as the training data, basin-to-basin variability in Dd is extrapolated globally. Our newly developed vector-based global hydrography, extracted from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation, is benchmarked against HydroSHEDS and selected high-quality regional hydrography datasets.


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Please refer to the following paper for the details of description of the database:

Lin, P., M. Pan, E. F. Wood, D. Yamazaki, and G. H. Allen, 2020: A new vector-based global river network dataset accounting for variable drainage density based on the latest spaceborne elevation data. Scientific Data, in review.

Contact Peirong Lin or Ming Pan for questions.

See Also

MERIT-Basins, Global Reach-level A priori Discharge Estimates for SWOT (GRADES), Global Reach-level Flood Reanalysis (GRFR)