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.
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.
the Creative Commons Attribution 4.0 License.
DAR-type model based on “long memory-threshold” structure: a competitor for daily streamflow prediction under changing environment
Huimin Wang
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
Songbai Song
College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling 712100, China
Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A & F University, Yangling 712100, China
Gengxi Zhang
CORRESPONDING AUTHOR
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
Thian Yew Gan
Department of Civil and Environmental Engineering, University of Alberta, Edmonton T6G2R3, Canada
Zhuoyue Peng
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
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Tianliang Jiang, Xiaoling Su, Gengxi Zhang, Te Zhang, and Haijiang Wu
Hydrol. Earth Syst. Sci., 27, 559–576, https://doi.org/10.5194/hess-27-559-2023, https://doi.org/10.5194/hess-27-559-2023, 2023
Short summary
Short summary
A hybrid method is developed for calculating the propagation probability of meteorological to ecological drought at different levels. Drought events are identified from a three-dimensional perspective. A spatial and temporal overlap rule is developed for extracting propagated drought events.
Huimin Wang, Gengxi Zhang, Shuyu Zhang, Xiaoling Su, Songbai Song, Lijie Shi, Kai Feng, and Xiaolei Fu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-217, https://doi.org/10.5194/hess-2022-217, 2022
Manuscript not accepted for further review
Short summary
Short summary
A significant increasing trend of compound dry and hot events has been reported in many regions under global warming. However, most of the proposed indices are based on monthly meteorological data and cannot monitor short-term events timely. This study proposes a novel daily-scale compound dry and hot index by jointing daily drought index and heat index. This index can detect spatial evolutions of dry and hot conditions and reflects vegetation losses, indicating its applicability.
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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.
This study introduces a novel dual-threshold double autoregressive (DTDAR) model for daily...