Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs

Selecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when DEMs. employs 36 sample sub-basins Hubei (China) province. Firstly, topography, normalized difference vegetation index (NDVI), water storage change are used building multiple regression models to calculate length. Power functions fit of each sub-basin. Nine randomly chosen regions served as test sub-basins. The results show that: (1) NDVI have high correlation with length, coefficient determination (R2) ranges between 0.85 0.87; (2) length obtained Multiple model using change, NDVI, topography influence factors is similar actual featuring equal 0.714; (3) calculates FATs sub-basin province, error 5.13%. Moreover, network extraction by mainly depends on thus being consistent regional water-resources change.