Articles | Volume 9, issue 1
https://doi.org/10.5194/esurf-9-105-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-9-105-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Mar 2021
Research article |
| 02 Mar 2021
| 02 Mar 2021
Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies
Richard Barnes
CORRESPONDING AUTHOR
Energy & Resources Group (ERG), University of California, Berkeley, USA
Electrical Engineering & Computer Science, University of California, Berkeley, USA
Berkeley Institute for Data Science (BIDS), University of California, Berkeley, USA
Kerry L. Callaghan
Department of Earth & Environmental Sciences, University of Minnesota, Minneapolis, USA
Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, USA
Andrew D. Wickert
Department of Earth & Environmental Sciences, University of Minnesota, Minneapolis, USA
Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, USA
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Cited
15 citations as recorded by crossref.
- Transfer learning with convolutional neural networks for hydrological streamline delineation N. Jaroenchai et al. 10.1016/j.envsoft.2024.106165
- Thalweg and Ridge Network Extraction From Unaltered Topographic Data as a Basis for Terrain Partitioning G. Moretti & S. Orlandini 10.1029/2022JF006943
- Example-based terrain synthesis with pit removal J. Scott & N. Dodgson 10.1016/j.cag.2021.06.012
- Land-surface parameters for spatial predictive mapping and modeling A. Maxwell & C. Shobe 10.1016/j.earscirev.2022.103944
- A network-based analysis of critical resource accessibility during floods M. Preisser et al. 10.3389/frwa.2023.1278205
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- The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling K. Callaghan et al. 10.5194/gmd-18-1463-2025
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- The role of climate and population change in global flood exposure and vulnerability J. Rogers et al. 10.1038/s41467-025-56654-8
- Drainage integration in extensional tectonic settings P. Larson et al. 10.1016/j.geomorph.2021.108082
- Topological Relationship‐Based Flow Direction Modeling: Stream Burning and Depression Filling C. Liao et al. 10.1029/2022MS003487
- Fill‐spill‐merge terrain analysis reveals topographical controls on Canadian river runoff N. Wagle & L. Smith 10.1002/hyp.15238
- GraphFlood 1.0: an efficient algorithm to approximate 2D hydrodynamics for landscape evolution models B. Gailleton et al. 10.5194/esurf-12-1295-2024
- GDBM: A database of global drainage basin morphology S. Grieve et al. 10.1371/journal.pone.0320771
- Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations R. Barnes et al. 10.5194/esurf-8-431-2020
14 citations as recorded by crossref.
- Transfer learning with convolutional neural networks for hydrological streamline delineation N. Jaroenchai et al. 10.1016/j.envsoft.2024.106165
- Thalweg and Ridge Network Extraction From Unaltered Topographic Data as a Basis for Terrain Partitioning G. Moretti & S. Orlandini 10.1029/2022JF006943
- Example-based terrain synthesis with pit removal J. Scott & N. Dodgson 10.1016/j.cag.2021.06.012
- Land-surface parameters for spatial predictive mapping and modeling A. Maxwell & C. Shobe 10.1016/j.earscirev.2022.103944
- A network-based analysis of critical resource accessibility during floods M. Preisser et al. 10.3389/frwa.2023.1278205
- CHONK 1.0: landscape evolution framework: cellular automata meets graph theory B. Gailleton et al. 10.5194/gmd-17-71-2024
- The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling K. Callaghan et al. 10.5194/gmd-18-1463-2025
- c-HAND: near real-time coastal flood mapping M. Wang et al. 10.3389/frwa.2024.1329109
- The role of climate and population change in global flood exposure and vulnerability J. Rogers et al. 10.1038/s41467-025-56654-8
- Drainage integration in extensional tectonic settings P. Larson et al. 10.1016/j.geomorph.2021.108082
- Topological Relationship‐Based Flow Direction Modeling: Stream Burning and Depression Filling C. Liao et al. 10.1029/2022MS003487
- Fill‐spill‐merge terrain analysis reveals topographical controls on Canadian river runoff N. Wagle & L. Smith 10.1002/hyp.15238
- GraphFlood 1.0: an efficient algorithm to approximate 2D hydrodynamics for landscape evolution models B. Gailleton et al. 10.5194/esurf-12-1295-2024
- GDBM: A database of global drainage basin morphology S. Grieve et al. 10.1371/journal.pone.0320771
Latest update: 11 May 2025
Short summary
Existing ways of modeling the flow of water amongst landscape depressions such as swamps and lakes take a long time to run. However, as our previous work explains, depressions can be quickly organized into a data structure – the depression hierarchy. This paper explains how the depression hierarchy can be used to quickly simulate the realistic filling of depressions including how they spill over into each other and, if they become full enough, how they merge into one another.
Existing ways of modeling the flow of water amongst landscape depressions such as swamps and...
