Climate Change Data Portal
| DOI | 10.1016/j.jhydrol.2024.130841 |
| Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments | |
| 发表日期 | 2024 |
| ISSN | 0022-1694 |
| EISSN | 1879-2707 |
| 起始页码 | 631 |
| 卷号 | 631 |
| 英文摘要 | Constrained by the sparsity of observational streamflow data, large-scale catchments face pressing challenges in streamflow prediction and flood management amid climate change. Deep learning excels in simulation performance while flow lag information in data-driven approaches is barely highlighted. In this study, we introduce a time-lag informed deep learning framework for large-scale catchments. Central to this framework is the utilization of flow time-lag information between upstream and downstream subbasins, enabling precise flood forecasting at outlet driven by upstream data. Taking the monsoon-influenced large-scale Dulong-Irrawaddy River Basin (DIRB) as study area, we determined peak flow lag (PFL) days and relative annual flow scale (RAFS) for defined subbasins. By incorporating this time-lag information with historical flow data at different time intervals, we developed the optimal model for DIRB. This model was then applied to evaluate the flood processes in 2008 and 2009, using selected flood indicators. The results indicate that the time-lag information led to significant performance improvements, notably in the LSTM_PFL_RAFS model driven by upstream Hkamti sub-basin data, which achieved a Kling-Gupta Efficiency (KGE) of 0.891 (Nash-Sutcliffe efficiency coefficient, NSE, 0.904), surpassing LSTM's 0.683 (NSE, 0.785). Further integration of historical flow data with specific interval, the optimal model, H(15)_PFL utilizes Hkamti sub-basin data, reached an impressive KGE of 0.948 (NSE, 0.940). This model outperformed standard LSTM in accurately simulating key flood characteristics, including peak flows, initiation times, and durations for the 2008 and 2009 flood events. Notably, H(15)_PFL provides a valuable 15day lead time for flood forecasting, extending the window for emergency response preparations. Future research that incorporates additional essential catchment features into the framework holds great potential in unraveling the complex mechanisms of hydrological responses to human activities and climate change. |
| 英文关键词 | Streamflow prediction; Flood early warning; Deep learning; Data sparsity; Large scale catchments; Transboundry River |
| 语种 | 英语 |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| WOS类目 | Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources |
| WOS记录号 | WOS:001182611400001 |
| 来源期刊 | JOURNAL OF HYDROLOGY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/290892 |
| 作者单位 | Yunnan University; Yunnan University; Tongji University |
| 推荐引用方式 GB/T 7714 | . Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments[J],2024,631. |
| APA | (2024).Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments.JOURNAL OF HYDROLOGY,631. |
| MLA | "Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments".JOURNAL OF HYDROLOGY 631(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
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。
