Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau

Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau

Abstract Interpretability of deep learning (DL) poses a significant challenge in hydrology modeling, particularly under the complex and frigid conditions of the Tibetan Plateau (TP), which further restricts its application. In this study, we developed a cascade‐style hybrid modeling framework by int...

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Bibliographic Details
Main Authors: Zhanliang Zhu, Xiongpeng Tang, Jianyun Zhang, Yehai Tang, Lei Liu, Chao Gao, Silong Zhang, Yanli Liu, Junliang Jin, Cuishan Liu, Bikui Zhao, Guoqing Wang
Format: Article
Language:English
Published: Wiley 2025-08-01
Series:Water Resources Research
Subjects:
deep learning
hybrid modeling
hydrological process
snowmelt runoff
Tibetan Plateau
Online Access:https://doi.org/10.1029/2024WR038515
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https://doi.org/10.1029/2024WR038515

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