Chao et al. (2026) Simulation of Extreme Flood Events Based on Precipitation Fusion: A Multi-Method Fusion Framework Combining RF and BMA

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Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-02-27
• Authors: Lijun Chao, Gang Li, Chao Yu, Sheng Wang, Ke Zhang, Guoqing Wang, Zhijia Li
• DOI: 10.3390/rs18050715

Research Groups

Not specified in the provided text.

Short Summary

This study introduces the Hybrid Downscaling and Multi-source Precipitation Fusion (HDMPF) framework to enhance the spatial resolution and accuracy of precipitation estimates, significantly improving simulations of extreme precipitation and hydrological responses.

Objective

• To develop and evaluate the Hybrid Downscaling and Multi-source Precipitation Fusion (HDMPF) framework to improve the spatial resolution and accuracy of precipitation estimates and enhance simulations of extreme precipitation and hydrological responses.

Study Configuration

• Spatial Scale: Basin scale
• Temporal Scale: Event-based (extreme precipitation and flood events)

Methodology and Data

• Models used: Hybrid Downscaling and Multi-source Precipitation Fusion (HDMPF) framework (comprising Radial Basis Function network, Random Forest, and Bayesian Model Averaging), Grid-Xin’anjiang model.
• Data sources: Multiple satellite precipitation products.

Main Results

• The HDMPF framework significantly improves spatiotemporal precipitation accuracy.
• The Kling-Gupta Efficiency (KGE) for precipitation estimates increased to 0.90–0.95.
• The Root Mean Square Error (RMSE) for precipitation estimates was reduced to below 0.3 mm/h.
• The framework accurately reproduces precipitation cores, peak intensities, flood peaks, timing, and multi-peak hydrographs.

Contributions

• Presents a novel Hybrid Downscaling and Multi-source Precipitation Fusion (HDMPF) framework that integrates downscaling and fusion techniques to improve precipitation data.
• Demonstrates significant advancements in the accuracy of high-resolution precipitation estimates and the simulation of extreme hydrological events.
• Offers a robust solution with strong potential for enhancing basin-scale hydrological modeling and flood early warning systems.

Funding

Not specified in the provided text.

Citation

@article{Chao2026Simulation,
  author = {Chao, Lijun and Li, Gang and Yu, Chao and Wang, Sheng and Zhang, Ke and Wang, Guoqing and Li, Zhijia},
  title = {Simulation of Extreme Flood Events Based on Precipitation Fusion: A Multi-Method Fusion Framework Combining RF and BMA},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18050715},
  url = {https://doi.org/10.3390/rs18050715}
}