A Novel Hydrological Signature-Informed Framework for Enhancing Streamflow Prediction Using Multi-Task Learning
文献类型：期刊论文
| 作者 | Wang, Zili2,3; Li, Chaoyue1,3; Wei, Ruilong2,3; Zhang, Binlan2,3; Cui, Peng2,3,4 |
| 刊名 | WATER RESOURCES RESEARCH
 |
| 出版日期 | 2026-01-16 |
| 卷号 | 62期号:1页码:e2025WR041485 |
| 关键词 | streamflow prediction hydrological signature-informed multi-task learning large-sample hydrological modeling disaster early warning |
| ISSN号 | 0043-1397 |
| DOI | 10.1029/2025WR041485 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Hydrological signatures (HS) have proven to be highly effective in calibrating physically-based hydrological models, enhancing their process consistency. However, their integration into parameter optimization for deep learning (DL)-based hydrological models has been limited. To address this gap, we propose a novel HS-informed framework that dynamically integrates HS into DL parameterization through a multi-task learning approach. This study evaluates the impact of HS integration on model performance using a large-scale, global hydrological data set. The HS-informed model achieved a significant performance improvement, with a median Nash-Sutcliffe Efficiency (NSE) of 0.739, compared to 0.666 for the baseline model across the test set. Notably, the most pronounced improvements in NSE were observed in hydrologically complex basins, including baseflow-dominated (+0.135), drought-prone (+0.148), and flood-prone basins (+0.159). Sensitivity analysis further revealed that the HS-informed model could leverage extended historical input data (over 120 days) to sustain robust performance (median NSE of 0.715) over a 30-day forecast period. Shapley Additive Explanations analysis highlighted two key mechanisms underlying these improvements: the enhanced recognition of long-term hydrological patterns through improved memory and a better representation of catchment heterogeneity by emphasizing non-climatic attributes. These findings demonstrate that integrating HS offers a superior approach to traditional point-error-based calibration in AI-driven hydrological modeling. |
| URL标识 | 查看原文 |
| WOS关键词 | LARGE-SAMPLE HYDROLOGY ; MODEL ; REGIONALIZATION ; BEHAVIORS ; BASINS ; CAMELS ; INDEX |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001662603500001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219717]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Li, Chaoyue; Cui, Peng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China; 2.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Engn Resilience, Chengdu, Peoples R China; 3.Univ Chinese Acad Sci, Beijing, Peoples R China; 4.CAS HEC, China Pakistan Joint Res Ctr Earth Sci, Islamabad, Pakistan |
| 推荐引用方式 GB/T 7714 | Wang, Zili,Li, Chaoyue,Wei, Ruilong,et al. A Novel Hydrological Signature-Informed Framework for Enhancing Streamflow Prediction Using Multi-Task Learning[J]. WATER RESOURCES RESEARCH,2026,62(1):e2025WR041485. |
| APA | Wang, Zili,Li, Chaoyue,Wei, Ruilong,Zhang, Binlan,&Cui, Peng.(2026).A Novel Hydrological Signature-Informed Framework for Enhancing Streamflow Prediction Using Multi-Task Learning.WATER RESOURCES RESEARCH,62(1),e2025WR041485. |
| MLA | Wang, Zili,et al."A Novel Hydrological Signature-Informed Framework for Enhancing Streamflow Prediction Using Multi-Task Learning".WATER RESOURCES RESEARCH 62.1(2026):e2025WR041485. |
入库方式： OAI收割
来源：地理科学与资源研究所
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