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 2, 2020

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. This 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
Alumni - Data Science Fellow