Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework
文献类型：期刊论文
| 作者 | Pan, Zhengke2,3; Liu, Pan2,3; Gao, Shida2,3; Xia, Jun1,2,3 ; Chen, Jie2,3; Cheng, Lei2,3
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| 刊名 | HYDROLOGY AND EARTH SYSTEM SCIENCES
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| 出版日期 | 2019-08-19 |
| 卷号 | 23期号:8页码:3405-3421 |
| ISSN号 | 1027-5606 |
| DOI | 10.5194/hess-23-3405-2019 |
| 通讯作者 | Liu, Pan(liupan@whu.edu.cn) |
| 英文摘要 | Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. Many existing studies model the time-varying parameters as functions of physically based covariates; however, a major challenge remains in finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes and one scenario with a stationary scheme for model parameters were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine the validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with the stationary setting of model parameters, (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance, and (3) model parameters calibrated over dry years were not suitable for predicting runoff over wet years because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions. |
| WOS关键词 | MODEL PARAMETERS ; UNGAUGED CATCHMENTS ; NON-STATIONARITY ; RUNOFF ; RAINFALL ; REGIONALIZATION ; UNCERTAINTY ; STREAMFLOW ; TRANSFERABILITY ; IDENTIFICATION |
| 资助项目 | National Key Research and Development Program[2018YFC0407202] ; National Natural Science Foundation of China[51861125102] ; National Natural Science Foundation of China[51879193] ; Natural Science Foundation of Hubei Province[2017CFA015] ; Innovation Team in Key Field of the Ministry of Science and Technology[2018RA4014] |
| WOS研究方向 | Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000481989200002 |
| 出版者 | COPERNICUS GESELLSCHAFT MBH |
| 资助机构 | National Key Research and Development Program ; National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province ; Innovation Team in Key Field of the Ministry of Science and Technology |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/68750]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Pan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Hubei, Peoples R China 3.Wuhan Univ, Hubei Prov Key Lab Water Syst Sci Sponge City Con, Wuhan, Hubei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pan, Zhengke,Liu, Pan,Gao, Shida,et al. Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework[J]. HYDROLOGY AND EARTH SYSTEM SCIENCES,2019,23(8):3405-3421. |
| APA | Pan, Zhengke,Liu, Pan,Gao, Shida,Xia, Jun,Chen, Jie,&Cheng, Lei.(2019).Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework.HYDROLOGY AND EARTH SYSTEM SCIENCES,23(8),3405-3421. |
| MLA | Pan, Zhengke,et al."Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework".HYDROLOGY AND EARTH SYSTEM SCIENCES 23.8(2019):3405-3421. |
入库方式： OAI收割
来源：地理科学与资源研究所
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