Real-time correction of channel-bed roughness and water level in river network hydrodynamic modeling for accurate forecasting
Open accessAbstract
The accuracy and reliability of hydrodynamic models are sensitive to both hydraulic state variables and model parameters, particularly the bed roughness, while their simultaneous real-time corrections and corresponding effects still need to be well-established and understood. This paper presents a real-time data assimilation model that corrects channel-bed roughness and water level in a river network hydrodynamic model, ensuring its accuracy and reliability. Experiments and parameter analysis evaluated the effect of initial roughness and observation noise level on model performance. Correcting both roughness and water level improved filtering time and forecasting accuracy by up to 63% and 80%, respectively, compared to methods only correcting water level. The filtering time was reduced by 44–63%, and the water level forecasting RMSE decreased by up to 80%. Both models experienced increased filtering time and forecasting error as observation noise increased, but the proposed model had a lower increase. With accurate hydraulic state measurement (e.g., 0.005 m error), the model achieved negligible water level forecasting error after 7 h of data assimilation. The model's accuracy depended on the initial channel-bed roughness, and the algorithm enables real-time roughness correction, making it useful for flood forecasting.
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Conventional remote sensing often struggles to distinguish complex riparian boundaries, and standard hydraulic models fail to ingest seasonally resolved phenological data.
“Specifically, conventional remote sensing often struggles to distinguish complex riparian boundaries, and standard hydraulic models fail to ingest seasonally resolved phenological data 23 .”
From AI-driven digital twinning quantifies the trade-off between flood conveyance and riparian stability under climate extremes (2026) · cites this paper as [23]
The Introduction discusses remote sensing as a way to calibrate hydraulic roughness and says constant model parameters can impair flood forecasts. It does not address difficulty distinguishing riparian boundaries or whether standard hydraulic models can ingest seasonally resolved phenological data; searches for those concepts found no supporting passage. The supplied copy is not truncated. (LLM, conf=0.76)
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Cited by
Sorted by citations
Shin, J. H., Go, S. H., & Park, J. H. (2026) · Scientific Reports · doi:10.1038/s41598-026-63704-8
- Authors
- Chen, Y., Cao, F., Cheng, W., Liu, B., & Yu, P.
- Affiliations
- Zhejiang Institute of Hydraulics & Estuary (CN)
- Zhejiang University of Technology (CN)
- Zhejiang University (CN)
- Changsha University of Science and Technology (CN)
- Corresponding author
- Chen, Yifan
- Venue
- Scientific Reports
- Published
- 2023-11-24
- Volume
- 13
- Issue
- 1
- Pages
- 20660-20660
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10673827
- OpenAlex
- W4388972416
- Cited by
- 6
- References
- 0
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 1
- FWCI
- 0.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (51609213, 12002310)
- General Scientific Research Projects of Education of Zhejiang Province (Y201941076)
- Zhejiang Key Research and Development Program (2021C03017)
- Natural Science Foundation of Zhejiang Province (LZ22E090003)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Global and Planetary Change › Flood Risk Assessment and Management
- Keywords
- real-time data assimilation, water level prediction, measurement noise, flood forecasting
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