River mapping using the shallow-water equations on GPU clusters: Costa Rica as a case study
DOI:
https://doi.org/10.29375/25392115.5103Keywords:
River mapping, Shallow water equations, GPU computingAbstract
Costa Rica’s River systems play a pivotal role in providing valuable resources for society. Given that the country is exposed to a water-dense, tropical climate, it is crucial to assess flooding risk and plan for extreme events. In this paper, the first step towards a river simulation pipeline is established using the Reventazón River as a case study. To achieve this, a High-Performance Computing – HPC portable shallow water equation solver was implemented. Boundary and initial conditions were set using QGIS and Python, and a simple Manning model was considered for friction. No rainfall, infiltration, or subsurface modeling was implemented in this work. The simulation yielded good qualitative results for water flow for the whole Reventazón River. More complex simulations are enabled with these results, given that an initial condition for water flow in the river was established.
Author Biographies
Daniel Caviedes-Voullième, Jülich Supercomputing Centre
Institute for Bio- and Geosciences: Agrosphere, Forschungszentrum Jülich
Esteban Meneses, Centro Nacional de Alta Tecnología
School of Computing, Costa Rica Institute of Technology
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