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

Pan, Zhengke; Liu, Pan; Gao, Shida; Xia, Jun; Chen, Jie; Cheng, Lei

doi:https://doi.org/10.5194/hess-23-3405-2019

Articles | Volume 23, issue 8

https://doi.org/10.5194/hess-23-3405-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-23-3405-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 8

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

Supplement

https://doi.org/10.5194/hess-23-3405-2019-supplement

Data sets

Collation of Australian Modeller's Streamflow Dataset for 780 Unregulated Australian Catchments Y. Q. Zhang, N. Viney, A. Frost, and A. Oke https://doi.org/10.4225/08/58b5baad4fcc2

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.