Improving trans-regional hydrological modelling by combining LSTM with big hydrological data
文献类型：期刊论文
| 作者 | Tang, Senlin3,4,5; Sun, Fubao3,4; Zhang, Qiang5; Singh, Vijay P.1,2; Feng, Yao4 |
| 刊名 | JOURNAL OF HYDROLOGY-REGIONAL STUDIES
 |
| 出版日期 | 2025-04-01 |
| 卷号 | 58页码:102257 |
| 关键词 | Hydrological modelling Ungauged basins Long short-term memory Global hydrological models Model migration |
| DOI | 10.1016/j.ejrh.2025.102257 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Study region: Lancang-Mekong River Basin (LMRB), Brazil. Study focus: Streamflow prediction in ungauged basins is a significant challenge in hydrology. This study investigates the transferability of deep learning models for hydrological simulations in ungauged basins, focusing on how constraints like catchment attributes, meteorological forcing, and Global Hydrological Models (GHMs) improve model performance when transferring knowledge from gauged to ungauged basins. We applied the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS-BR) dataset alongside GHMs and deep learning techniques to simulate hydrological processes in the LMRB. New hydrological insights for the region: The results demonstrate that a post-processing scheme combining deep learning, meteorological data, and GHMs significantly improves model accuracy, achieving a median Nash-Sutcliffe Efficiency (NSE) of 0.64, compared to 0.50 for the baseline Long Short-Term Memory (LSTM) model without GHMs. Key factors influencing model performance include catchment attributes, climate variations, and the length of the modelling series. A notable finding is the importance of catchment attributes in defining hydrological similarity, which enhances model migration between regions with differing data availability. Cross-regional migration was particularly successful when hydrological similarities between the Amazon Basin and LMRB were evaluated, achieving an NSE of 0.86 at the Pakse hydrological station. These insights provide a novel modelling framework for hydrological simulations in data-scarce regions, emphasizing the role of physical mechanisms and hydrological similarities in improving model transferability. |
| URL标识 | 查看原文 |
| WOS关键词 | HYDROMETEOROLOGICAL TIME-SERIES ; LARGE-SAMPLE ; LANDSCAPE ATTRIBUTES ; CATCHMENTS ; ASSIMILATION ; VARIABILITY |
| WOS研究方向 | Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001429446400001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213244]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Sun, Fubao; Zhang, Qiang |
| 作者单位 | 1.UAE Univ, Natl Water & Energy Ctr, Al Ain, U Arab Emirates 2.Texas A&M Univ, Dept Biol & Agr Engn, Zachry Dept Civil & Environm Engn, College Stn, TX USA; 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China; 5.Beijing Normal Univ, Adv Interdisciplinary Inst Environm & Ecol, Guangdong Prov Key Lab Wastewater Informat Anal &, Zhuhai 519087, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Tang, Senlin,Sun, Fubao,Zhang, Qiang,et al. Improving trans-regional hydrological modelling by combining LSTM with big hydrological data[J]. JOURNAL OF HYDROLOGY-REGIONAL STUDIES,2025,58:102257. |
| APA | Tang, Senlin,Sun, Fubao,Zhang, Qiang,Singh, Vijay P.,&Feng, Yao.(2025).Improving trans-regional hydrological modelling by combining LSTM with big hydrological data.JOURNAL OF HYDROLOGY-REGIONAL STUDIES,58,102257. |
| MLA | Tang, Senlin,et al."Improving trans-regional hydrological modelling by combining LSTM with big hydrological data".JOURNAL OF HYDROLOGY-REGIONAL STUDIES 58(2025):102257. |
入库方式： OAI收割
来源：地理科学与资源研究所
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