Comparison and integration of hydrological models and machine learning models in global monthly streamflow simulation
文献类型：期刊论文
| 作者 | Zhang, Jiawen7; Kong, Dongdong5,6; Li, Jianfeng4; Qiu, Jianxiu3; Zhang, Yongqiang2; Gu, Xihui1; Guo, Meiyu7 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2025-04-01 |
| 卷号 | 650页码:17 |
| 关键词 | Streamflow simulation Machine learning Hydrological model Multi-model weighting ensemble |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2024.132549 |
| 通讯作者 | Kong, Dongdong(kongdongdong@cug.edu.cn) ; Li, Jianfeng(jianfengli@cuhk.edu.hk) |
| 英文摘要 | With the advancement of machine learning technology, machine learning models (MLMs) are progressively emerging as a pivotal branch in streamflow simulation. However, compared to traditional hydrological models (HMs), a comprehensive understanding of their strengths and applicability across diverse climatic regions worldwide remains elusive. This study compares the performance of four widely used lumped HMs (GR2M, XAJ, SAC, and Alpine) with four prevalent MLMs (RF, GBDT, DNN, and CNN) across 16,218 catchments worldwide. Results show that MLMs can't always surpass HMs. Although the percentage of qualified-level accuracy (Kling-Gupta efficiency, KGE >= 0.2) is higher in the MLMs group, the HMs have a higher percentage of good-level accuracy (KGE > 0.6). HMs outperform MLMs in Southeastern North America, Western Europe, and most regions of the Southern Hemisphere. To combine the merits of HMs and MLMs, the performance of seven multi-model weighting ensemble methods (MWEs) is evaluated. The optimal MWE is employed to unify the results of four HMs and four MLMs, further improving the simulation accuracy. The MWEs improve the simulation accuracy effectively and the Inverse Rank Prediction Combination (InvW) is the best-performing MWE, which elevates the percentage of qualified accuracy by 13 % and 6 % for the best-performing HM and MLM, respectively. Despite the improvements with InvW, correlation analyses of KGE with catchment humidity index (HI), mean temperature (Tair), and leaf area index (LAI) reveal that simulating streamflow in dry, cold, and lower vegetated areas remains challenging (R-HI = 0.36, R-Tair = 0.1, and R-LAI = 0.19). Additionally, the precision of streamflow simulations diminishes in areas heavily impacted by human activities, primarily due to hydraulic engineering and water withdrawals. Our study systematically evaluates the performance of different HMs and MLMs in different regions, proposes a framework to combine the merits of HMs and MLMs, and sheds light on the constraining factors of accurate streamflow simulation. |
| WOS关键词 | CLIMATE ; PERFORMANCE ; IMPACT ; DECOMPOSITION ; OPTIMIZATION ; COMBINATION ; CATCHMENT ; PROSPECTS ; DATASET ; BASIN |
| 资助项目 | National Key R & D Program of China[2022YFC3002804] ; National Natural Science Foundation of China[42071055] ; Key research project of Hubei Hydrology and Water Resources Center[HBSWKY202406] ; Research Grants Council of the Hong Kong Special Administrative Region, China[CUHK12301220] ; Open Fund of National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan, China[2022KFJJ05] ; Natural Science Foundation of Wuhan[2024040801020275] ; [2023YFE0103900] ; [42101052] ; [RFS2223-2H02] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001394149300001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key R & D Program of China ; National Natural Science Foundation of China ; Key research project of Hubei Hydrology and Water Resources Center ; Research Grants Council of the Hong Kong Special Administrative Region, China ; Open Fund of National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan, China ; Natural Science Foundation of Wuhan |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212570]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Kong, Dongdong; Li, Jianfeng |
| 作者单位 | 1.China Univ Geosci, Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, 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.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Peoples R China 4.Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong, Peoples R China 5.Ctr Severe Weather & Climate & Hydrogeol Hazards, Wuhan 430074, Peoples R China 6.China Univ Geosci, Sch Environm Studies, Dept Atmospher Sci, Wuhan 430074, Peoples R China 7.Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Jiawen,Kong, Dongdong,Li, Jianfeng,et al. Comparison and integration of hydrological models and machine learning models in global monthly streamflow simulation[J]. JOURNAL OF HYDROLOGY,2025,650:17. |
| APA | Zhang, Jiawen.,Kong, Dongdong.,Li, Jianfeng.,Qiu, Jianxiu.,Zhang, Yongqiang.,...&Guo, Meiyu.(2025).Comparison and integration of hydrological models and machine learning models in global monthly streamflow simulation.JOURNAL OF HYDROLOGY,650,17. |
| MLA | Zhang, Jiawen,et al."Comparison and integration of hydrological models and machine learning models in global monthly streamflow simulation".JOURNAL OF HYDROLOGY 650(2025):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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