Estimation of time-varying parameter in Budyko framework using long short-term memory network over the Loess Plateau, China & nbsp;
文献类型：期刊论文
| 作者 | Wang, Feiyu2; Xia, Jun2,3; Zou, Lei2; Zhan, Chesheng4; Liang, Wei1 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 607页码:13 |
| 关键词 | Budyko framework Land use change Long short-term memory network Time-varying Loess Plateau |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2022.127571 |
| 通讯作者 | Zou, Lei(zoulei@igsnrr.ac.cn) |
| 英文摘要 | Accurate estimation of the basin-specific parameter in the Budyko framework (e.g., parameter n in the Choudhury-Yang equation) is critical to quantify precipitation partitioning into evapotranspiration (E) and runoff. However, n is difficult to estimate due to complex interactions between the water balance and various environmental factors. In this study, we identified the controlling factors of n using random forest during 1981-2015 for 30 basins in the Loess Plateau of China. We then used the long short-term memory (LSTM) network combined with an 11-year moving window to develop a model to estimate time-varying n. This model was further incorporated into the Choudhury-Yang equation to simulate E. Our results showed that correlations between parameter n and environmental factors presented obvious spatial heterogeneity. Three land use type factors (i.e., the proportions of cropland, shrubland, and built-up land area), two climatic factors (i.e., precipi-tation and potential evapotranspiration), and a water use factor (i.e., irrigation water use) were identified as the controlling factors for n. Based on these controls, the LSTM model outperformed the traditional multiple linear regression model (MLR model) in estimating time-varying n, with root mean square error (RMSE) of 0.31/0.49 and coefficient of determination (R-2) of 0.88/0.67 for the LSTM/MLR model, respectively. Moreover, compared with the original Choudhury-Yang equation (using constant n calibrated by long-term average water balance), the improved equation (using time-varying n estimated by the LSTM) better reproduced the time series of water balance-based E. This study could enhance the applicability of the Budyko framework and provide scientific guidance for water resources management.& nbsp; |
| WOS关键词 | VEGETATION CHANGES ; CLIMATE-CHANGE ; SPATIOTEMPORAL VARIATIONS ; ECOLOGICAL RESTORATION ; WATER-RESOURCES ; RUNOFF ; BALANCE ; EVAPOTRANSPIRATION ; EVAPORATION ; DYNAMICS |
| 资助项目 | National Natural Science Founda-tion of China[42101043] ; National Natural Science Founda-tion of China[41771118] ; China Postdoctoral Science Foundation[2021M703178] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040304] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000788683300004 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Founda-tion of China ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/176045]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zou, Lei |
| 作者单位 | 1.Shaanxi Normal Univ, Sch Geog & Tourism, Xian 710119, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 3.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 |
Wang, Feiyu,Xia, Jun,Zou, Lei,et al. Estimation of time-varying parameter in Budyko framework using long short-term memory network over the Loess Plateau, China & nbsp; [J]. JOURNAL OF HYDROLOGY,2022,607:13. |
| APA |
Wang, Feiyu,Xia, Jun,Zou, Lei,Zhan, Chesheng,&Liang, Wei.(2022). Estimation of time-varying parameter in Budyko framework using long short-term memory network over the Loess Plateau, China & nbsp; .JOURNAL OF HYDROLOGY,607,13. |
| MLA |
Wang, Feiyu,et al." Estimation of time-varying parameter in Budyko framework using long short-term memory network over the Loess Plateau, China & nbsp; ".JOURNAL OF HYDROLOGY 607(2022):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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