2022 CSDMS meeting-067


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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.