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. We propose a novel way to measure and analyze networks of drainage dividesfrom digital elevation models. We developed an algorithm that extractsdrainage divides based on the drainage basin boundaries defined by a streamnetwork. In contrast to streams, there is no straightforward approach toorder and classify divides, although it is intuitive that some divides aremore important than others. A meaningful way of ordering divides is theaverage distance one would have to travel down on either side of a divide toreach a common stream location. However, because measuring these distancesis computationally expensive and prone to edge effects, we instead sortdivide segments based on their tree-like network structure, starting fromendpoints at river confluences. The sorted nature of the network allowsfor assigning distances to points along the divides, which can be shown to scalewith the average distance downslope to the common stream location.Furthermore, because divide segments tend to have characteristic lengths, anordering scheme in which divide orders increase by 1 at junctions mimicsthese distances. We applied our new algorithm to the Big Tujunga catchmentin the San Gabriel Mountains of southern California and studied themorphology of the drainage divide network. Our results show that topographicmetrics, like the downstream flow distance to a stream and hillslope relief,attain characteristic values that depend on the drainage area threshold usedto derive the stream network. Portions along the divide network that havelower than average relief or are closer than average to streams are oftendistinctly asymmetric in shape, suggesting that these divides are unstable.Our new and automated approach thus helps to objectively extract and analyzedivide networks from digital elevation models.

Drainage divide networks – Part 1: Identification and ordering in digital elevation models