中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models

文献类型：期刊论文

作者Bai, Peng; Liu, Xiaomang; Xie, Jiaxin
刊名JOURNAL OF HYDROLOGY
出版日期2021
卷号592页码:11
关键词Hydrologic models Machine learning Runoff simulation LSTM
ISSN号0022-1694
DOI10.1016/j.jhydrol.2020.125779
通讯作者Bai, Peng(baip@igsnrr.ac.cn) ; Liu, Xiaomang(liuxm@igsnrr.ac.cn)
英文摘要Hydrologic models are commonly used to assess climate change impact on water resources. Several studies have reported that hydrologic models often experience severe performance degradation under climatic conditions different from calibration periods. With the advancement of artificial intelligence technology, the long short-term memory (LSTM) network has recently shown great potentials in rainfall-runoff modeling. However, little is known about the robustness of the LSTM network when used in changing climatic conditions. In this study, we compare the robustness of the LSTM network and two conceptual hydrologic models in runoff prediction in changing climatic conditions in 278 Model Parameter Estimation Experiment (MOPEX) basins. For calibration periods, the two hydrologic models have better performance in wet periods than in dry periods, while the LSTM network shows little performance difference under different climatic conditions. For validation periods, the three models suffer the largest performance loss when calibrated in a wet period and validated in a dry period. The performance losses of the LSTM network are primarily affected by the climate contrast between calibration and validation periods, while the performance losses of the two hydrologic models are mainly dependent on the climatic condition of validation periods. We also find that the length of the calibration period is an important factor affecting the relative performance of the models. Increasing the length of the calibration period has little effect on the validation performance of the two hydrologic models but enhances the LSTM network's performance. If sufficient calibration data is available, the LSTM network is a preferred tool for runoff simulation. On the other hand, the hydrologic models could have more advantages over the LSTM network in case of limited calibration data available.
WOS关键词CHANGE IMPACTS ; PERFORMANCE ; CALIBRATION ; STREAMFLOW ; LENGTH ; EVAPOTRANSPIRATION ; UNCERTAINTY ; CATCHMENTS ; TRANSFERABILITY ; STATIONARITY
资助项目National Key Research and Development Programe of China[2018YFA0605404] ; Natural Science Foundation of China[51979263] ; Natural Science Foundation of China[41922050] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2018067]
WOS研究方向Engineering ; Geology ; Water Resources
语种英语
WOS记录号WOS:000639844900028
出版者ELSEVIER
资助机构National Key Research and Development Programe of China ; Natural Science Foundation of China ; Youth Innovation Promotion Association, Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/162582]  
专题中国科学院地理科学与资源研究所
通讯作者Bai, Peng; Liu, Xiaomang
作者单位Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Bai, Peng,Liu, Xiaomang,Xie, Jiaxin. Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models[J]. JOURNAL OF HYDROLOGY,2021,592:11.
APA Bai, Peng,Liu, Xiaomang,&Xie, Jiaxin.(2021).Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models.JOURNAL OF HYDROLOGY,592,11.
MLA Bai, Peng,et al."Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models".JOURNAL OF HYDROLOGY 592(2021):11.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

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。