Computing water flow through complex landscapes, Part 2: Finding hierarchies in depressions and morphological segmentations

Richard Barnes, Kerry L. Callaghan, and Andrew D. Wickert

Earth Surface Dynamics
June 17, 2019

Abstract. Depressions – inwardly-draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous depressions also arise in image processing and morphological segmentation where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks, in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. The hierarchy can be used to selectively fill or breach depressions, or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on Github and Zenodo.

Featured Fellows

Richard Barnes

Energy & Resources Group, EECS
BIDS Alum – Data Science Fellow