Enhancing Hydrological Extremes Forecasting Capabilities in Data‐Scarce Regions Through Transfer Learning With Data Augmentation

作者：Yehai Tang, Xiongpeng Tang, Zhanliang Zhu, Chao Gao, Lei Liu, Fubo Zhao, Silong Zhang · 发表于：Earth s Future · 年份：2025 · DOI：10.1029/2025ef006060 · 被引用次数：15 · 研究领域：Hydrological Forecasting Using AI、Hydrology and Watershed Management Studies、Flood Risk Assessment and Management

Abstract Hydrological extremes forecasting in data‐scarce basins remains a longstanding challenge in hydrological science. Despite significant advancements in transferring hydrological knowledge from data‐rich to data‐sparse basins, such as regionalization techniques for hydrological prediction and novel deep learning (DL)‐based Transfer learning (TL) methods, the application of models trained in data‐rich basins introduces inevitable noise into predictions within data‐sparse basins. This potential distortion could misinterpret rainfall‐runoff patterns within specific basins. This study introduces a TL framework based on data augmentation (DA‐TL) within the context of hydrological modeling. The framework employs augmented rainfall data as input for conceptual models to generate pretraining runoff samples, addressing the challenges of sample scarcity and imbalance in target basins. Subsequently, TL is applied to fine‐tune predictions in the target basin, thereby mitigating inappropriate hydrological knowledge transfer associated with cross‐basin learning. The DA‐TL framework was validated across nine river basins in China, representing three distinct climate zones (semi‐arid, semi‐humid, and humid regions). Results indicate that the DA‐TL approach outperforms current DL methods for regionalized hydrological modeling. Specifically, under varying data scarcity scenarios, DA‐TL achieved average Nash–Sutcliffe Efficiency improvements of 3.8% and 1.0% compared to similar‐basin mode...