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

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

doi:https://doi.org/10.5194/hess-28-1147-2024

Articles | Volume 28, issue 5

https://doi.org/10.5194/hess-28-1147-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-28-1147-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 5

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

Data sets

Shuttle Radar Topography Mission (SRTM) Global NASA Shuttle Radar Topography Mission (SRTM) https://doi.org/10.5069/G9445JDF

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Index of /data/cmorph-high-resolution-global-precipitation-estimates NOAA https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/

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 Mohammed Abdallah http://www.hydroshare.org/resource/d0d9140845144d73ac578d865411a10a

Model code and software

vinereg: D-Vine Quantile Regression Thomas Nagler https://tnagler.github.io/vinereg/

Package 'quantreg' R. Koenker et al. https://cran.r-project.org/web/packages/quantreg/

BMA: an R package for Bayesian model averaging Adrian Raftery et al. https://cran.r-project.org/web/packages/BMA

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.