Articles | Volume 23, issue 8
https://doi.org/10.5194/hess-23-3405-2019
https://doi.org/10.5194/hess-23-3405-2019
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, Pan Liu, Shida Gao, Jun Xia, Jie Chen, and Lei Cheng

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Cited articles

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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, https://doi.org/10.1002/2016wr018850, 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, https://doi.org/10.1002/2015wr017327, 2016. 
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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.