Articles | Volume 30, issue 6
https://doi.org/10.5194/hess-30-1543-2026
https://doi.org/10.5194/hess-30-1543-2026
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

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Cited articles

Adnan, R. M., Meshram, S. G., Mostafa, R. R., Towfiqul IsIam, A. R. M., Abba, S. I., Andorful, F., and Chen, Z. H.: Application of soft computing models in streamflow forecasting, P. I. Civil Eng.-Wat. M., 172, 123–134, https://doi.org/10.1680/jwama.16.00075, 2019. 
Beven, K.: Deep learning, hydrological processes and the uniqueness of place, Hydrol. Process., 34, 3608–3613, https://doi.org/10.1002/hyp.13805, 2020. 
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity, J. Econometrics, 31, 307–327, https://doi.org/10.1016/0304-4076(86)90063-1, 1986. 
Broock, W. A., Scheinkman, J. A., Dechert, W. D., and LeBaron, B.: A test for independence based on the correlation dimension, Economet. Rev., 15, 197–235, https://doi.org/10.1080/07474939608800353, 1996. 
Can, İ., Tosunoğlu, F., and Kahya, E.: Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin, Turkey, Hydrol. Process., 26, 567–576, https://doi.org/10.1111/j.1747-6593.2012.00337.x, 2012. 
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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.
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