Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 1
Articles | Volume 30, issue 1
https://doi.org/10.5194/hess-30-205-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-205-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 1
Research article
 | 
14 Jan 2026
Research article |  | 14 Jan 2026

Multi-Machine Learning Ensemble Regionalization of Hydrological Parameters for Enhancing Flood Prediction in Ungauged Mountainous Catchments

Kai Li, Linmao Guo, Genxu Wang, Jihui Gao, Xiangyang Sun, Peng Huang, Jinlong Li, Jiapei Ma, and Xinyu Zhang

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Short summary
We propose a multi-machine learning ensemble—integrating Gradient Boosting Machine, K-Nearest Neighbors, and Extremely Randomized Trees (GBM-KNN-ERT)—to improve Topography-Based Subsurface Storm Flow (Top-SSF) parameter regionalization for flood prediction in ungauged catchments. Validated across 80 Chinese catchments, the ensemble achieved a Nash-Sutcliffe Efficiency (NSE) greater than 0.9 for 90 % of catchments, showing superior robustness to climate and donor variability.
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