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DOI	10.5194/hess-23-3405-2019
	Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework
	Pan Z.; Liu P.; Gao S.; Xia J.; Chen J.; Cheng L.
发表日期	2019
ISSN	1027-5606
起始页码	3405
结束页码	3421
卷号	23 期号:8
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).
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
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159625
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 S...
推荐引用方式 GB/T 7714	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],2019,23(8).
APA	Pan Z.,Liu P.,Gao S.,Xia J.,Chen J.,&Cheng L..(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).
MLA	Pan Z.,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).