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HESS | Articles | Volume 23, issue 8
Hydrol. Earth Syst. Sci., 23, 3405–3421, 2019
https://doi.org/10.5194/hess-23-3405-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 3405–3421, 2019
https://doi.org/10.5194/hess-23-3405-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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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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, https://doi.org/10.1029/2005wr004745, 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, https://doi.org/10.1002/2017wr020403, 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, https://doi.org/10.1016/j.jhydrol.2012.11.012, 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, 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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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.
Understanding the projection performance of hydrological models under contrasting climatic...
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