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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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Reference

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 peirongl@princeton.edu or Ming Pan mpan@princeton.edu for questions.

See Also

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