Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short-Term Memory (LSTM) Networks
文献类型：期刊论文
| 作者 | Xie, Jiaxin1,2; Liu, Xiaomang2; Tian, Wei1,2; Wang, Kaiwen2; Bai, Peng2; Liu, Changming2 |
| 刊名 | WATER RESOURCES RESEARCH
 |
| 出版日期 | 2022-08-01 |
| 卷号 | 58期号:8页码:19 |
| 关键词 | baseflow time series simulation deep learning long short-term memory |
| ISSN号 | 0043-1397 |
| DOI | 10.1029/2021WR031663 |
| 通讯作者 | Liu, Xiaomang(liuxm@igsnrr.ac.cn) |
| 英文摘要 | Accurate baseflow estimation is essential for ecological protection and water resources management. Past studies have used environmental predictors to extend baseflow from gauged basins to ungauged basins, publishing several regional or global datasets on mean annual baseflow. However, time series datasets of baseflow are still lacking due to the complexity of baseflow generation processes. Here, we developed a monthly baseflow data set using a Deep learning model called the long short-term memory (LSTM) networks. To better train the networks across basins, we compared the standard LSTM architecture using 8 time series as inputs with four variant architectures using 16 additional static properties as inputs. Dividing the contiguous United States into nine ecoregions, we used baseflow calculated from 1,604 gauged basins as training targets to calibrate the five LSTM architectures for each ecoregion separately. Results show that three variant architectures (Joint, Front, and Entity-Aware-LSTM) perform better than the standard LSTM, with median Kling-Gupta Efficiencies across basins greater than 0.85. Based on Front LSTM, the monthly baseflow data set with 0.25 degrees spatial resolution across the contiguous United States from 1981 to 2020 was obtained. Potential applications of the data set include analyzing baseflow trends under global change and estimating large-scale groundwater recharge. |
| WOS关键词 | STREAMFLOW OBSERVATIONS ; AUTOMATED TECHNIQUES ; DATA ASSIMILATION ; SURFACE-WATER ; UNITED-STATES ; FLOW INDEX ; TRENDS ; SOIL ; GROUNDWATER ; PATTERNS |
| 资助项目 | National Natural Science Foundation of China[41922050] ; Youth Innovation Promotion Association, CAS[2018067] |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000855257000047 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | National Natural Science Foundation of China ; Youth Innovation Promotion Association, CAS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/184950]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Xiaomang |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Jiaxin,Liu, Xiaomang,Tian, Wei,et al. Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short-Term Memory (LSTM) Networks[J]. WATER RESOURCES RESEARCH,2022,58(8):19. |
| APA | Xie, Jiaxin,Liu, Xiaomang,Tian, Wei,Wang, Kaiwen,Bai, Peng,&Liu, Changming.(2022).Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short-Term Memory (LSTM) Networks.WATER RESOURCES RESEARCH,58(8),19. |
| MLA | Xie, Jiaxin,et al."Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short-Term Memory (LSTM) Networks".WATER RESOURCES RESEARCH 58.8(2022):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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