Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 6
Articles | Volume 30, issue 6
https://doi.org/10.5194/hess-30-1543-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1543-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 6
Research article
 | 
25 Mar 2026
Research article |  | 25 Mar 2026

DAR-type model based on “long memory-threshold” structure: a competitor for daily streamflow prediction under changing environment

Huimin Wang, Songbai Song, Gengxi Zhang, Thian Yew Gan, and Zhuoyue Peng

Viewed

Total article views: 3,291 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,611 570 110 3,291 26 85 118
  • HTML: 2,611
  • PDF: 570
  • XML: 110
  • Total: 3,291
  • Supplement: 26
  • BibTeX: 85
  • EndNote: 118
Views and downloads (calculated since 28 May 2025)
Cumulative views and downloads (calculated since 28 May 2025)

Viewed (geographical distribution)

Total article views: 3,291 (including HTML, PDF, and XML) Thereof 3,286 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 06 Jun 2026
Download
Short summary
This study introduces a novel dual-threshold double autoregressive (DTDAR) model for daily streamflow prediction. The DTDAR model outperforms other commonly used models, especially when using a Student's t distribution for residuals, showing improved accuracy in capturing non-linearity and long-term memory in streamflow data.
Read more
Share