2022 CSDMS meeting-099
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The Depression Hierarchy and Python Bindings: Quantifying Internally-Drained Regions
Campbell Dunn,
(she/her),University of Wisconsin Madison Wisconsin, United States. cndunn@wisc.edu
Richard Barnes, Lawrence Berkeley National Laboratory Berkeley California, United States. rbarnes@umn.edu
Andrew Wickert, University of Minnesota Minneapolis Minnesota, United States. awickert@umn.edu
Kerry Callaghan, Columbia University New York New York, United States. kerryc@ldeo.columbia.edu
“Depression” is a blanket term used for inwardly draining regions of digital elevation models, for noise, and features of interest in image processing and morphological segmentation. Previously, depressions have presented topological and modeling difficulties so they were algorithmically removed and "hydrologically corrected" digital elevation models (DEMs) in place of DEMs of the naturally occurring Earth surface. Our novel data structure – the depression hierarchy – encodes these depressions into landscapes so that their full complexity is exposed and workable. Individual sub-depressions are successively merged together into meta-depressions in a process that continues until they drain externally. This is mapped out in a forest of binary trees algorithmically. This hierarchy can be used to manipulate individual depressions or depression networks, as well as to accelerate dynamic models of hydrological flow. While the algorithm was originally implemented in C++, in this work a Python wrapper has been built using the pybind11 library, allowing for the dynamic passage of C++ input/output files through Python. This enables users to capitalize on the strengths of both languages. The Python wrapper also streamlines the process of integrating the depression hierarchy into the CSDMS model interfaces and into Landlab. Here, we outline the structure and mechanics of the depression hierarchy algorithm and its accompanying Python wrapper.
Open-source code is available on GitHub at:
https://github.com/r-barnes/Barnes2019-DepressionHierarchy andhttps://github.com/r-barnes/pydephier
