05 May 2020
05 May 2020
Computing water flow through complex landscapes, Part 3: Fill-Spill-Merge: Flow routing in depression hierarchies
- 1Energy & Resources Group (ERG), University of California, Berkeley, USA
- 2Electrical Engineering & Computer Science, University of California, Berkeley, USA
- 3Berkeley Institute for Data Science (BIDS), University of California, Berkeley, USA
- 4Department of Earth & Environmental Sciences, University of Minnesota, Minneapolis, USA
- 5Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, USA
- 1Energy & Resources Group (ERG), University of California, Berkeley, USA
- 2Electrical Engineering & Computer Science, University of California, Berkeley, USA
- 3Berkeley Institute for Data Science (BIDS), University of California, Berkeley, USA
- 4Department of Earth & Environmental Sciences, University of Minnesota, Minneapolis, USA
- 5Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, USA
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 a Fill-Spill-Merge algorithm that utilizes our depression hierarchy 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 as well as results from two sample study areas. In these case studies, the algorithm runs 90–2600× faster (with a 2000–63 000× reduction in compute time) than the commonly-used Jacobi iteration and produces a more accurate output. Complete, well-commented, open-source code is available on Github and Zenodo.
Richard Barnes et al.


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RC1: 'review of esurf-2020-31', Anonymous Referee #1, 26 May 2020
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RC2: 'Hobley MS review', Daniel Hobley, 29 May 2020
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AC1: 'Author's Response', Richard Barnes, 18 Sep 2020


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RC1: 'review of esurf-2020-31', Anonymous Referee #1, 26 May 2020
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RC2: 'Hobley MS review', Daniel Hobley, 29 May 2020
-
AC1: 'Author's Response', Richard Barnes, 18 Sep 2020
Richard Barnes et al.
Model code and software
Fill-Spill-Merge Source Code R. Barnes and K. Callaghan https://doi.org/10.5281/zenodo.3755142
Richard Barnes et al.
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