Abstract
Depressions - inwardly draining regions - are common to many landscapes. When there is sufficient moisture, depressions take the form of lakes and wetlands; otherwise, they may be dry. Hydrological flow models used in geomorphology, hydrology, planetary science, soil and water conservation, and other fields often eliminate depressions through filling or breaching; however, this can produce unrealistic results. Models that retain depressions, on the other hand, are often undesirably expensive to run. In previous work we began to address this by developing a depression hierarchy data structure to capture the full topographic complexity of depressions in a region. Here, we extend this work by presenting the Fill-Spill-Merge algorithm that utilizes our depression hierarchy data structure to rapidly process and distribute runoff. Runoff fills depressions, which then overflow and spill into their neighbors. If both a depression and its neighbor fill, they merge. We provide a detailed explanation of the algorithm and results from two sample study areas. In these case studies, the algorithm runs 90-2600 times faster (with a reduction in compute time of 2000-63 000 times) than the commonly used Jacobi iteration and produces a more accurate output. Complete, well-commented, open-source code with 97 % test coverage is available on GitHub and Zenodo.
| Original language | English (US) |
|---|---|
| Article number | 7 |
| Pages (from-to) | 105-121 |
| Number of pages | 17 |
| Journal | Earth Surface Dynamics |
| Volume | 9 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 2 2021 |
Bibliographical note
Funding Information:Kerry L. Callaghan was supported by the National Science Foundation under grant no. EAR-1903606, the University of Minnesota Department of Earth Sciences Junior F Hayden Fellowship, the University of Minnesota Department of Earth Sciences H.E. Wright Footsteps Award, and start-up funds awarded to Andrew Wickert by the University of Minnesota.
Funding Information:
Acknowledgements. Richard Barnes was supported by the Department of Energy’s Computational Science Graduate Fellowship (grant no. DE-FG02-97ER25308) and, through the Berkeley Institute for Data Science’s PhD Fellowship, by the Gordon and Betty Moore Foundation (grant GBMF3834) as well as by the Alfred P. Sloan Foundation (grant 2013-10-27).
Funding Information:
Financial support. This research has been supported by the U.S. Department of Energy–Krell Institute (grant no. DE-FG02-97ER25308), the National Science Foundation Office of Advanced Cyberinfrastructure (grant no. ACI-1053575), the Gordon and Betty Moore Foundation (grant no. GBMF3834), the Alfred P. Sloan Foundation (grant no. 2013-10-27), and the National Science Foundation Division of Earth Sciences (grant no. EAR-1903606).
Funding Information:
This research has been supported by the U.S. Department of Energy-Krell Institute (grant no. DE-FG02-97ER25308), the National Science Foundation Office of Advanced Cyberinfrastructure (grant no. ACI-1053575), the Gordon and Betty Moore Foundation (grant no. GBMF3834), the Alfred P. Sloan Foundation (grant no. 2013-10-27), and the National Science Foundation Division of Earth Sciences (grant no. EAR-1903606).
Publisher Copyright:
© Author(s) 2021.
