Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins
文献类型：期刊论文
| 作者 | Tang, Senlin5; Sun, Fubao2,3,4,5; Liu, Wenbin; Wang, Hong; Feng, Yao; Li, Ziwei5 |
| 刊名 | WATER RESOURCES RESEARCH
 |
| 出版日期 | 2023-07-01 |
| 卷号 | 59期号:7页码:e2022WR034352 |
| 关键词 | long short-term memory global hydrological models hydrological similarities streamflow ungauged basins |
| ISSN号 | 0043-1397 |
| DOI | 10.1029/2022WR034352 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short-Term Memory (LSTM) is widely used to for such predictions, owing to its excellent migration performance. Traditional LSTM forced by meteorological data and catchment attribute data barely highlight the optimum data integration strategy for LSTM and its migration from data-rich basins to ungauged ones. In this study, we experimented with 1,897 global catchments and found that LSTM-corrected Global Hydrological Models (GHMs) outperformed uncorrected GHMs, improving the median Nash-Sutcliff efficiency (NSE) from 0.03 to 0.66. Notably, there was a large gap between traditional LSTM modeling in ungauged basins and autoregressive modeling in data-rich basins, and GHM-forced LSTM were an effective way to close this gap in ungauged basins. The spatial heterogeneity of the performance of GHM-forced LSTM was mainly influenced by three metrics (dryness, the leaf area index and latitude), which described the hydrological similarity among catchments. Weaker hydrological similarity among continental catchments results in larger variability in GHM-forced LSTM, with the best performance in Siberia (NSE, 0.54) and the worst in North America (NSE, 0.10). However, the migration performance of GHM-forced LSTM was significantly improved (NSE, 0.63) in ungauged basins when hydrological similarity was considered. This study stressed the advantages of GHM-forced LSTM and due significance should be attached to hydrological similarities among catchments to improve hydrological prediction in ungauged catchments. |
| WOS关键词 | CATCHMENTS ; MODEL ; MAP |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| WOS记录号 | WOS:001028913500001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194358]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Ctr Water Resources Res, Beijing, Peoples R China 2.Akesu Natl Stn Observat & Res Oasis Agroecosyst, Akesu, Peoples R China 3.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tang, Senlin,Sun, Fubao,Liu, Wenbin,et al. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins[J]. WATER RESOURCES RESEARCH,2023,59(7):e2022WR034352. |
| APA | Tang, Senlin,Sun, Fubao,Liu, Wenbin,Wang, Hong,Feng, Yao,&Li, Ziwei.(2023).Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins.WATER RESOURCES RESEARCH,59(7),e2022WR034352. |
| MLA | Tang, Senlin,et al."Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins".WATER RESOURCES RESEARCH 59.7(2023):e2022WR034352. |
入库方式： OAI收割
来源：地理科学与资源研究所
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