Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1147-2024
https://doi.org/10.5194/hess-28-1147-2024
Research article
 | 
07 Mar 2024
Research article |  | 07 Mar 2024

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Mohammed Abdallah, Ke Zhang, Lijun Chao, Abubaker Omer, Khalid Hassaballah, Kidane Welde Reda, Linxin Liu, Tolossa Lemma Tola, and Omar M. Nour

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Cited articles

Aas, K., Czado, C., Frigessi, A., and Bakken, H.: Pair-copula constructions of multiple dependence, Insurance, 44, 182–198, https://doi.org/10.1016/j.insmatheco.2007.02.001, 2009. 
Abdalla, E. M. H., Pons, V., Stovin, V., De-Ville, S., Fassman-Beck, E., Alfredsen, K., and Muthanna, T. M.: Evaluating different machine learning methods to simulate runoff from extensive green roofs, Hydrol. Earth Syst. Sci., 25, 5917–5935, https://doi.org/10.5194/hess-25-5917-2021, 2021. 
Abdallah, M.: A D-vine copula-based quantile regression towards merging satellite precipitation products over a rugged topography: A case study at the upper Tekeze Atbara Basin of the Nile Basin, HydroShare [data set], http://www.hydroshare.org/resource/d0d9140845144d73ac578d865411a10a (last access: 25 February 2024), 2024. 
Abdallah, M., Mohammadi, B., Zaroug, M. A. H., Omer, A., Cheraghalizadeh, M., Eldow, M. E. E., and Duan, Z.: Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models, J. Hydrol.: Reg. Stud., 44, 101259, https://doi.org/10.1016/j.ejrh.2022.101259, 2022. 
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Short summary
A D-vine copula-based quantile regression (DVQR) model is used to merge satellite precipitation products. The performance of the DVQR model is compared with the simple model average and one-outlier-removed average methods. The nonlinear DVQR model outperforms the quantile-regression-based multivariate linear and Bayesian model averaging methods.
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