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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Mar 2024
Research article |
| 07 Mar 2024
| 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
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
The Hydraulics Research Station, P.O. Box 318, Wad Madani, Republic of the Sudan
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210024, China
China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu, 210024, China
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Lijun Chao
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu, 210024, China
Abubaker Omer
Moon Soul Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
Khalid Hassaballah
IGAD Climate Prediction and Applications Center (ICPAC), Nairobi, Kenya
Kidane Welde Reda
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Tigray Agricultural Research Institute, Mekele, Ethiopia
Linxin Liu
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Tolossa Lemma Tola
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Omar M. Nour
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
The Hydraulics Research Station, P.O. Box 318, Wad Madani, Republic of the Sudan
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Hydrol. Earth Syst. Sci., 29, 3703–3725, https://doi.org/10.5194/hess-29-3703-2025, https://doi.org/10.5194/hess-29-3703-2025, 2025
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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.
A D-vine copula-based quantile regression (DVQR) model is used to merge satellite precipitation...
