Incorporating multi-timescale data into a single long short-term memory network to enhance reservoir-regulated streamflow simulation
文献类型：期刊论文
| 作者 | Lang, Lichen1,3; Gao, Xing3; Li, Yongkun2; Li, Zhihui1,3; Wu, Feng1,3 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2025-06-01 |
| 卷号 | 654页码:132806 |
| 关键词 | Streamflow simulation Human-regulated watersheds Multi-timescale data MTS-LSTM |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2025.132806 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Water security and its sustainable management are critical to human survival and livelihoods. Under the pressures of climate change and population growth, an increasing number of natural watersheds are being regulated by dams and reservoirs. However, the operation of these man-made infrastructures, particularly small-scale reservoirs managed by local governments, is often characterized by flexible and irregular management practices, significantly complicating streamflow modeling-a critical aspect of water management. Remote sensing provides valuable reservoir storage data for data-scarce basins, but its coarse temporal resolution requires integration with ground-based observations or simulations. Unlike traditional physical models that rely on explicit hydrological processes and predefined reservoir operation rules, or data-driven methods that struggle with multi-timescale data and their dependencies, the Multi-TimeScale Long Short-Term Memory (MTS-LSTM) model is a deep learning framework designed to integrate multi-timescale data. This study evaluates the MTSLSTM in integrating monthly remote sensing-derived reservoir storage variation data to simulate daily reservoir-regulated streamflow. The case study on the Yuanjiang River Basin demonstrated that the MTS-LSTM effectively bridges the gap between SWAT-simulated natural streamflow and observed regulated streamflow, a gap primarily caused by reservoir storage variations. The model achieved strong performance at two hydrological stations. For monthly simulations, the mean Correlation Coefficient (CC) was 0.92, Nash-Sutcliffe Efficiency (NSE) was 0.81, and Kling-Gupta Efficiency (KGE) was 0.80. For daily simulations, the mean CC was 0.79, NSE was 0.58, and KGE was 0.71. Integrating remote sensing data significantly enhances simulation accuracy, outperforming naive LSTM models. This study presents a systematic methodology for incorporating multi-source and multi-timescale data to enhance the accuracy of reservoir-regulated streamflow simulations, with a particular focus on regions with limited data and hybrid cascade reservoir systems. |
| URL标识 | 查看原文 |
| WOS关键词 | CLIMATE-CHANGE ; OPERATION |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001427557100001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213217]  |
| 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 | Wu, Feng |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Beijing Water Sci & Technol Inst, Beijing, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Lang, Lichen,Gao, Xing,Li, Yongkun,et al. Incorporating multi-timescale data into a single long short-term memory network to enhance reservoir-regulated streamflow simulation[J]. JOURNAL OF HYDROLOGY,2025,654:132806. |
| APA | Lang, Lichen,Gao, Xing,Li, Yongkun,Li, Zhihui,&Wu, Feng.(2025).Incorporating multi-timescale data into a single long short-term memory network to enhance reservoir-regulated streamflow simulation.JOURNAL OF HYDROLOGY,654,132806. |
| MLA | Lang, Lichen,et al."Incorporating multi-timescale data into a single long short-term memory network to enhance reservoir-regulated streamflow simulation".JOURNAL OF HYDROLOGY 654(2025):132806. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。