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...
Saved in:
| Main Authors: | Z. Tan, D. Xu, S. Taraphdar, J. Ma, G. Bisht, L. R. Leung |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-08-01
|
| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/29/3833/2025/hess-29-3833-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
High-resolution GNSS troposphere tomography through explainable deep learning-based downscaling framework
by: Saeid Haji-Aghajany, et al.
Published: (2025-08-01) -
A New GRACE Downscaling Approach for Deriving High‐Resolution Groundwater Storage Changes Using Ground‐Based Scaling Factors
by: Huixiang Li, et al.
Published: (2024-11-01) -
A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation
by: Jonathan Schmidt, et al.
Published: (2025-07-01) -
A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions
by: Yongkang Li, et al.
Published: (2025-01-01) -
Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya
by: Asnake Kassahun Abebe, et al.
Published: (2025-05-01)