Reach Scale Hdyrology

A machine learning approach to estimating drainage density (D_d ) 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 D_d 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.