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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2026
Research article |
| 14 Jan 2026
| 14 Jan 2026
Multi-Machine Learning Ensemble Regionalization of Hydrological Parameters for Enhancing Flood Prediction in Ungauged Mountainous Catchments
Kai Li
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
Linmao Guo
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
Genxu Wang
CORRESPONDING AUTHOR
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
Jihui Gao
CORRESPONDING AUTHOR
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
Xiangyang Sun
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
Peng Huang
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
Jinlong Li
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
Jiapei Ma
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
Xinyu Zhang
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610000, China
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Hydrol. Earth Syst. Sci., 29, 5267–5282, https://doi.org/10.5194/hess-29-5267-2025, https://doi.org/10.5194/hess-29-5267-2025, 2025
Short summary
Short summary
We investigated preferential flow paths and ground layers in coniferous and broadleaf forests in valley moraines along an elevation gradient. The results show that the percentage of preferential flow paths involved in subsurface flow was relatively low and comparable in both forests, mainly driven by vegetation-related properties. The presence of the ground layer facilitates rapid lateral flow towards downslope positions, leading to earlier and greater peak flow.
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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.
We propose a multi-machine learning ensemble—integrating Gradient Boosting Machine, K-Nearest...
