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

Data sets

Collation of Australian Modeller's Streamflow Dataset for 780 Unregulated Australian Catchments Y. Q. Zhang, N. Viney, A. Frost, and A. Oke

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.