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. 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.

Computing water flow through complex landscapes – Part 1: Incorporating depressions in flow routing using FlowFill

“…Simple topographically driven flow-routing approaches are the most popular because they are quick to compute (e.g. Braun and Willett, 2013;Gallant and Wilson, 1996;Schwanghart and Scherler, 2014), agnostic to the amount of rainfall or runoff applied, and applicable over length scales from puddles (e.g. Chu et al, 2013) and small catchments (e.g.…”

CHONK 1.0: landscape evolution framework: cellular automata meets graph theory

“…It is worth noting that some algorithms have been specifically develop to explicitly process, calculate and fill depressions with arbitrarily given amount of water (e.g. L. Barnes et al, 2019Barnes et al, , 2021. However, these methods are only designed to fill pits with water and would require significant amount of modifications to be utilised as cellular automata processor, or even to any other purpose than what they are designed for.…”

“…Perron, 2011); using priority queue data structures to traverse dynamically the graph of cells (e.g. Barnes et al, 2014bBarnes et al, , 2019. Computing a topological order allows us to process cells one by one as requested by our model design.…”

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

“…The algorithms of Barnes et al (2014Barnes 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.…”