Articles | Volume 23, issue 8
Hydrol. Earth Syst. Sci., 23, 3405–3421, 2019
Hydrol. Earth Syst. Sci., 23, 3405–3421, 2019
Research article
19 Aug 2019
Research article | 19 Aug 2019

Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

Zhengke Pan et al.

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Hydrol. Earth Syst. Sci. Discuss.,,, 2022
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The influence of a prolonged meteorological drought on catchment water storage capacity: a hydrological-model perspective
Zhengke Pan, Pan Liu, Chong-Yu Xu, Lei Cheng, Jing Tian, Shujie Cheng, and Kang Xie
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Cited articles

Ajami, N. K., Duan, Q. Y., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403,, 2007. 
Bracken, C., Holman, K. D., Rajagopalan, B., and Moradkhani, H.: A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis, Water Resour. Res., 54, 243–255,, 2018. 
Brigode, P., Oudin, L., and Perrin, C.: Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?, J. Hydrol., 476, 410–425,, 2013. 
Broderick, C., Matthews, T., Wilby, R. L., Bastola, S., and Murphy, C.: Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods, Water Resour. Res., 52, 8343–8373,, 2016. 
Cha, Y., Park, S. S., Lee, H. W., and Stow, C. A.: A Bayesian hierarchical approach to model seasonal algal variability along an upstream to downstream river gradient, Water Resour. Res., 52, 348–357,, 2016. 
Short summary
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. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.