An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics
<p>Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hy...
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Copernicus Publications
2025-08-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/29/3833/2025/hess-29-3833-2025.pdf |
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| author | Z. Tan D. Xu S. Taraphdar J. Ma G. Bisht L. R. Leung |
| author_facet | Z. Tan D. Xu S. Taraphdar J. Ma G. Bisht L. R. Leung |
| author_sort | Z. Tan |
| collection | DOAJ |
| description | <p>Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian process learning (LSG) model, we developed a downscaling approach to construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The high-resolution (as fine as 30 <span class="inline-formula">m</span> resolution) and low-resolution (mostly 1000 <span class="inline-formula">m</span> resolution) meshes include 664 724 and 14 536 grid cells, respectively. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.</p> |
| format | Article |
| id | doaj-art-8b8de177e1e2493c8d2670ab3e7ba31e |
| institution | Kabale University |
| issn | 1027-5606 1607-7938 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj-art-8b8de177e1e2493c8d2670ab3e7ba31e2025-08-20T04:03:16ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382025-08-01293833385210.5194/hess-29-3833-2025An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamicsZ. Tan0D. Xu1S. Taraphdar2J. Ma3G. Bisht4L. R. Leung5Pacific Northwest National Laboratory, Richland, WA 99352, USAPacific Northwest National Laboratory, Richland, WA 99352, USAPacific Northwest National Laboratory, Richland, WA 99352, USACollege of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USAPacific Northwest National Laboratory, Richland, WA 99352, USAPacific Northwest National Laboratory, Richland, WA 99352, USA<p>Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian process learning (LSG) model, we developed a downscaling approach to construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The high-resolution (as fine as 30 <span class="inline-formula">m</span> resolution) and low-resolution (mostly 1000 <span class="inline-formula">m</span> resolution) meshes include 664 724 and 14 536 grid cells, respectively. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.</p>https://hess.copernicus.org/articles/29/3833/2025/hess-29-3833-2025.pdf |
| spellingShingle | Z. Tan D. Xu S. Taraphdar J. Ma G. Bisht L. R. Leung An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics Hydrology and Earth System Sciences |
| title | An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics |
| title_full | An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics |
| title_fullStr | An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics |
| title_full_unstemmed | An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics |
| title_short | An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics |
| title_sort | efficient hybrid downscaling framework to estimate high resolution river hydrodynamics |
| url | https://hess.copernicus.org/articles/29/3833/2025/hess-29-3833-2025.pdf |
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