Articles | Volume 29, issue 16
https://doi.org/10.5194/hess-29-3833-2025
https://doi.org/10.5194/hess-29-3833-2025
Research article
 | 
18 Aug 2025
Research article |  | 18 Aug 2025

An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics

Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung

Model code and software

Code and test data for "An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics" Z. Tan et al. https://doi.org/10.5281/zenodo.14258083

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Short summary
Flow depth and velocity determine various river functions, but their high-resolution simulations are expensive. Here, we developed a downscaling approach that can provide fast and accurate estimation of high-resolution river hydrodynamics. The 84-fold acceleration achieved by the method makes reliable flood risk analysis that needs hundreds or thousands of model runs feasible. More importantly, it provides an opportunity to couple large-scale hydrodynamics with local processes in river models.
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