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

Abstract

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.

LakeCC: a tool for efficiently identifying lake basins with application to palaeogeographic reconstructions of North America

“…One possibility to overcome this issue is to remove all depressions from the DEM by filling them (Barnes et al ., ; Barnes, ). Other, even more elaborate methods, create a drainage hierarchy (Barnes et al ., ). The focus of this study is the identification of lake basins, without including advanced flow routing techniques.…”

Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies

“…provide a more thorough literature review, which we briefly recap here. A hierarchical segmentation by Beucher (1994) did not produce a data structure on which flow could be routed. Salembier and Pardas (1994) generated a hierarchical segmentation by repeatedly simplifying source images; hydrologically speaking, this can lead to unacceptable degradation of terrain information.…”

A Rainfall‐Based, Sequential Depression‐Filling Algorithm and Assessments on a Watershed in Northeastern Indiana, USA

“…(2014); Barnes et al. (2020) utilize a wet surface definition of depressions in their development of filling algorithms optimized for the case, where all depressions must be filled, disregarding sequence. Cordonnier et al.…”

“…The algorithms of Barnes et al (2014); Barnes et al (2020) utilize a wet surface definition of depressions in their development of filling algorithms optimized for the case, where all depressions must be filled, disregarding sequence. Cordonnier et al ( 2019) used a minimum spanning tree (MST) approach to find the depression hierarchy.…”