2022 CSDMS meeting-067: Difference between revisions
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|CSDMS meeting abstract title= | |CSDMS meeting abstract title=Fill-Spill-Merge: Fast flow modeling in nested depressions | ||
|Working_group_member_WG_FRG=Terrestrial Working Group, Cyberinformatics and Numerics Working Group | |Working_group_member_WG_FRG=Terrestrial Working Group, Cyberinformatics and Numerics Working Group | ||
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|CSDMS meeting coauthor first name abstract=Kerry | |||
|CSDMS meeting coauthor last name abstract=Callaghan | |||
|CSDMS meeting coauthor institute / Organization=Lamont-Doherty Earth Observatory | |||
|CSDMS meeting coauthor town-city=Palisades | |||
|CSDMS meeting coauthor country=United States | |||
|State=New York | |||
|CSDMS meeting coauthor email address=kerrylee37@gmail.com | |||
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|CSDMS meeting coauthor first name abstract=Andrew | |||
|CSDMS meeting coauthor last name abstract=Wickert | |||
|CSDMS meeting coauthor institute / Organization=University of Minnesota | |||
|CSDMS meeting coauthor town-city=Minneapolis | |||
|CSDMS meeting coauthor country=United States | |||
|State=Minnesota | |||
|CSDMS meeting coauthor email address=awickert@umn.edu | |||
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{{CSDMS meeting abstract template 2022 | {{CSDMS meeting abstract template 2022 | ||
|CSDMS meeting abstract= | |CSDMS meeting abstract=Depressions—inwardly-draining regions—are common to many landscapes. When there is sufficient water availability, depressions take the form of lakes and wetlands; otherwise, they may be dry. Depressions can be hard to model, so hydrological flow models often eliminate them through filling or breaching, producing unrealistic results. However, models that retain depressions are often undesirably expensive to run. Our Depression Hierarchy poster shows how we began to address this by developing a data structure to capture the full topographic complexity of depressions in a region. Here, we present a Fill-Spill-Merge algorithm that utilizes depression hierarchies 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. In case studies, the algorithm runs 90–2,600× faster (with a 2,000–63,000× reduction in compute time) than commonly-used iterative methods and produces a more accurate output. Complete, well-commented, open-source code with 97% test coverage is available on Github and Zenodo. | ||
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Latest revision as of 17:27, 15 April 2022
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Fill-Spill-Merge: Fast flow modeling in nested depressions
Richard Barnes,
(He/Him/His),Lawrence Berkeley National Lab Berkeley California, United States. rijard.barnes@gmail.com
Kerry Callaghan, Lamont-Doherty Earth Observatory Palisades New York, United States. kerrylee37@gmail.com
Andrew Wickert, University of Minnesota Minneapolis Minnesota, United States. awickert@umn.edu
Depressions—inwardly-draining regions—are common to many landscapes. When there is sufficient water availability, depressions take the form of lakes and wetlands; otherwise, they may be dry. Depressions can be hard to model, so hydrological flow models often eliminate them through filling or breaching, producing unrealistic results. However, models that retain depressions are often undesirably expensive to run. Our Depression Hierarchy poster shows how we began to address this by developing a data structure to capture the full topographic complexity of depressions in a region. Here, we present a Fill-Spill-Merge algorithm that utilizes depression hierarchies 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. In case studies, the algorithm runs 90–2,600× faster (with a 2,000–63,000× reduction in compute time) than commonly-used iterative methods and produces a more accurate output. Complete, well-commented, open-source code with 97% test coverage is available on Github and Zenodo.
