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

Viewed

Total article views: 2,874 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,626 1,188 60 2,874 187 72 75
  • HTML: 1,626
  • PDF: 1,188
  • XML: 60
  • Total: 2,874
  • Supplement: 187
  • BibTeX: 72
  • EndNote: 75
Views and downloads (calculated since 04 Feb 2019)
Cumulative views and downloads (calculated since 04 Feb 2019)

Viewed (geographical distribution)

Total article views: 2,874 (including HTML, PDF, and XML) Thereof 2,337 with geography defined and 537 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 May 2024
Download
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.