2022 CSDMS meeting-099: Difference between revisions
From CSDMS
No edit summary |
Cnicoledunn (talk | contribs) No edit summary |
||
| Line 24: | Line 24: | ||
{{CSDMS meeting abstract title temp2022 | {{CSDMS meeting abstract title temp2022 | ||
|CSDMS meeting abstract title=The Depression Hierarchy and Python Bindings: Quantifying Internally-Drained Regions | |CSDMS meeting abstract title=The Depression Hierarchy and Python Bindings: Quantifying Internally-Drained Regions | ||
|Working_group_member_WG_FRG=Terrestrial Working Group, Hydrology Focus Research Group | |Working_group_member_WG_FRG=Terrestrial Working Group, Hydrology Focus Research Group, Critical Zone Focus Research Group | ||
}} | }} | ||
{{CSDMS meeting authors template | {{CSDMS meeting authors template | ||
| Line 47: | Line 47: | ||
|CSDMS meeting coauthor first name abstract=Kerry | |CSDMS meeting coauthor first name abstract=Kerry | ||
|CSDMS meeting coauthor last name abstract=Callaghan | |CSDMS meeting coauthor last name abstract=Callaghan | ||
|CSDMS meeting coauthor institute / Organization= | |CSDMS meeting coauthor institute / Organization=Lamont-Doherty Earth Observatory | ||
|CSDMS meeting coauthor town-city= | |CSDMS meeting coauthor town-city=Palisades | ||
|CSDMS meeting coauthor country=United States | |CSDMS meeting coauthor country=United States | ||
|State=New York | |State=New York | ||
Revision as of 18:38, 15 April 2022
(if you haven't already)
Log in (or create account for non-CSDMS members)
Forgot username? Search or email:CSDMSweb@colorado.edu
Browse abstracts
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, Lamont-Doherty Earth Observatory Palisades New York, United States. kerryc@ldeo.columbia.edu
Depressions are inwardly-draining regions of digital elevation models (DEMs). For modeling purposes, depressions are often removed to create a "hydrologically corrected" DEM. However, this compromises model realism and creates perfectly flat surfaces that must be handled in some other way. If depressions are not removed, the movement of water within them must be modeled. This is challenging because depressions are often deeply nested, one inside the other.
Here, we present a novel data structure – the depression hierarchy – which uses a forest of binary trees to capture and abstract the full topographic and the topologic complexity of depressions. The depression hierarchy can be used to quickly manipulate individual depressions or depression networks, as well as to accelerate dynamic models of hydrological flow, as shown in our Fill-Spill-Merge poster. While the algorithm is implemented in C++ for performance reasons, we have also developed a Python wrapper using the pybind11 library. 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 Landlab. Open source code is available on GitHub at https://github.com/r-barnes/Barnes2019-DepressionHierarchy and https://github.com/r-barnes/pydephier.
