Articles | Volume 8, issue 2
https://doi.org/10.5194/esurf-8-431-2020
© Author(s) 2020. 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-8-431-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jun 2020
Research article |
| 02 Jun 2020
| 02 Jun 2020
Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations
Richard Barnes
CORRESPONDING AUTHOR
Energy & Resources Group (ERG), University of California, Berkeley, USA
Electrical Engineering and 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
Andrew D. Wickert
Department of Earth & Environmental Sciences, University of Minnesota, Minneapolis, USA
Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, USA
Viewed
Total article views: 7,632 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 17 Jun 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 5,965 | 1,503 | 164 | 7,632 | 179 | 228 |
- HTML: 5,965
- PDF: 1,503
- XML: 164
- Total: 7,632
- BibTeX: 179
- EndNote: 228
Total article views: 5,883 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Jun 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,863 | 887 | 133 | 5,883 | 132 | 178 |
- HTML: 4,863
- PDF: 887
- XML: 133
- Total: 5,883
- BibTeX: 132
- EndNote: 178
Total article views: 1,749 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 17 Jun 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,102 | 616 | 31 | 1,749 | 47 | 50 |
- HTML: 1,102
- PDF: 616
- XML: 31
- Total: 1,749
- BibTeX: 47
- EndNote: 50
Viewed (geographical distribution)
Total article views: 7,632 (including HTML, PDF, and XML)
Thereof 7,028 with geography defined
and 604 with unknown origin.
Total article views: 5,883 (including HTML, PDF, and XML)
Thereof 5,392 with geography defined
and 491 with unknown origin.
Total article views: 1,749 (including HTML, PDF, and XML)
Thereof 1,636 with geography defined
and 113 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
18 citations as recorded by crossref.
- Numerical modeling of groundwater‐driven stream network evolution in low‐relief post‐glacial landscapes C. Cullen et al. https://doi.org/10.1002/esp.5278
- Thalweg and Ridge Network Extraction From Unaltered Topographic Data as a Basis for Terrain Partitioning G. Moretti & S. Orlandini https://doi.org/10.1029/2022JF006943
- Identification of Playa Lakes and tracking their evolution pathways using geochemical models in the Great Indian Thar desert M. Kumar et al. https://doi.org/10.1016/j.scitotenv.2023.169250
- A Rainfall‐Based, Sequential Depression‐Filling Algorithm and Assessments on a Watershed in Northeastern Indiana, USA S. Noel et al. https://doi.org/10.1029/2020MS002362
- From data modification to preservation: evolution of DEM depression processing algorithms in hydrological modelling L. Ma et al. https://doi.org/10.1007/s12665-026-12971-9
- Depression detection in billion-scale DEM grids in digital terrain analysis using HRBTree L. Ma et al. https://doi.org/10.1007/s12145-025-02050-1
- The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling K. Callaghan et al. https://doi.org/10.5194/gmd-18-1463-2025
- Evaluation of Priority Queues in the Priority Flood Algorithm for Hydrological Modelling L. Ma et al. https://doi.org/10.3390/w17223202
- Drainage integration in extensional tectonic settings P. Larson et al. https://doi.org/10.1016/j.geomorph.2021.108082
- FastFlow: GPU Acceleration of Flow and Depression Routing for Landscape Simulation A. Jain et al. https://doi.org/10.1111/cgf.15243
- GDBM: A database of global drainage basin morphology S. Grieve et al. https://doi.org/10.1371/journal.pone.0320771
- Land-surface parameters for spatial predictive mapping and modeling A. Maxwell & C. Shobe https://doi.org/10.1016/j.earscirev.2022.103944
- CHONK 1.0: landscape evolution framework: cellular automata meets graph theory B. Gailleton et al. https://doi.org/10.5194/gmd-17-71-2024
- Modified Priority-Flood algorithm for hydrologic modelling-based digital terrain analysis using a hash heap structure L. Ma et al. https://doi.org/10.1080/19475683.2026.2617191
- Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies R. Barnes et al. https://doi.org/10.5194/esurf-9-105-2021
- Mountain process regime characterization using a topographic morphological structural framework B. Young & M. Bishop https://doi.org/10.1080/02723646.2021.2020968
- Segmentation of Soil Surface Roughness Features in High-Resolution DEMS E. Vannier et al. https://doi.org/10.3390/agriculture16101070
- Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index M. Preisser et al. https://doi.org/10.5194/hess-26-3941-2022
18 citations as recorded by crossref.
- Numerical modeling of groundwater‐driven stream network evolution in low‐relief post‐glacial landscapes C. Cullen et al. https://doi.org/10.1002/esp.5278
- Thalweg and Ridge Network Extraction From Unaltered Topographic Data as a Basis for Terrain Partitioning G. Moretti & S. Orlandini https://doi.org/10.1029/2022JF006943
- Identification of Playa Lakes and tracking their evolution pathways using geochemical models in the Great Indian Thar desert M. Kumar et al. https://doi.org/10.1016/j.scitotenv.2023.169250
- A Rainfall‐Based, Sequential Depression‐Filling Algorithm and Assessments on a Watershed in Northeastern Indiana, USA S. Noel et al. https://doi.org/10.1029/2020MS002362
- From data modification to preservation: evolution of DEM depression processing algorithms in hydrological modelling L. Ma et al. https://doi.org/10.1007/s12665-026-12971-9
- Depression detection in billion-scale DEM grids in digital terrain analysis using HRBTree L. Ma et al. https://doi.org/10.1007/s12145-025-02050-1
- The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling K. Callaghan et al. https://doi.org/10.5194/gmd-18-1463-2025
- Evaluation of Priority Queues in the Priority Flood Algorithm for Hydrological Modelling L. Ma et al. https://doi.org/10.3390/w17223202
- Drainage integration in extensional tectonic settings P. Larson et al. https://doi.org/10.1016/j.geomorph.2021.108082
- FastFlow: GPU Acceleration of Flow and Depression Routing for Landscape Simulation A. Jain et al. https://doi.org/10.1111/cgf.15243
- GDBM: A database of global drainage basin morphology S. Grieve et al. https://doi.org/10.1371/journal.pone.0320771
- Land-surface parameters for spatial predictive mapping and modeling A. Maxwell & C. Shobe https://doi.org/10.1016/j.earscirev.2022.103944
- CHONK 1.0: landscape evolution framework: cellular automata meets graph theory B. Gailleton et al. https://doi.org/10.5194/gmd-17-71-2024
- Modified Priority-Flood algorithm for hydrologic modelling-based digital terrain analysis using a hash heap structure L. Ma et al. https://doi.org/10.1080/19475683.2026.2617191
- Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies R. Barnes et al. https://doi.org/10.5194/esurf-9-105-2021
- Mountain process regime characterization using a topographic morphological structural framework B. Young & M. Bishop https://doi.org/10.1080/02723646.2021.2020968
- Segmentation of Soil Surface Roughness Features in High-Resolution DEMS E. Vannier et al. https://doi.org/10.3390/agriculture16101070
- Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index M. Preisser et al. https://doi.org/10.5194/hess-26-3941-2022
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
Maps of elevation are used to help predict the flow of water so we can better understand landslides, floods, and global climate change. However, modeling the flow of water is difficult when elevation maps include swamps, lakes, and other depressions. This paper explains a new method that overcomes these difficulties, allowing models to run faster and more accurately.
Maps of elevation are used to help predict the flow of water so we can better understand...
