globalchange  > 气候变化与战略
DOI: 10.5194/hess-23-3405-2019
论文题名:
Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework
作者: Pan Z.; Liu P.; Gao S.; Xia J.; Chen J.; Cheng L.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2019
卷: 23, 期:8
起始页码: 3405
结束页码: 3421
语种: 英语
Scopus关键词: Catchments ; Decision making ; Runoff ; Hierarchical bayesian ; Hydrological modeling ; Performance degradation ; Projection uncertainty ; Southeast australia ; Time varying parameter ; Time-varying functions ; Transferability of hydrological models ; Time varying control systems ; Bayesian analysis ; climate conditions ; decision making ; hierarchical system ; hydrological modeling ; performance assessment ; regression analysis ; runoff ; spatial analysis
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. © 2019 The Author(s).
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162915
Appears in Collections:气候变化与战略

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作者单位: Pan, Z., State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China, Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, Hubei, China; Liu, P., State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China, Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, Hubei, China; Gao, S., State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China, Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, Hubei, China; Xia, J., State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China, Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, Hubei, China, Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Chen, J., State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China, Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, Hubei, China; Cheng, L., State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China, Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, Hubei, China

Recommended Citation:
Pan Z.,Liu P.,Gao S.,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,2019-01-01,23(8)
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