Digital elevation models (DEMs) have reached resolutions and sizes that only parallel computers can efficiently process. One important application of DEMs is predicting how much water flows where, the so-called ``flow routing problem'' (a variation of which is the problem of determining the drainage area upstream of a point in a DEM). The traditional algorithm for flow routing is sequential, and attempts to parallelize this method have so far only been moderately successful. Herein, we build on earlier work in Richardson, Hill, and Perron (2014) and propose an algorithm and several variations that can efficiently solve the flow routing problem on very large models with very large numbers of parallel processes. For the largest model we use, with 1.88 billion points, the best algorithm herein can route water in 4.0 seconds on 12,888 processes of a computer cluster.