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.
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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Cited
8 citations as recorded by crossref.
- Evolution of precipitation-runoff-sediment dependence under environmental change: A Vine Copula analysis of two Loess Plateau basins J. Li et al. https://doi.org/10.1016/j.ejrh.2026.103505
- Bidirectional Extreme Response Analysis for a Synchronized-Period Evaluation of Multiple Precipitation Datasets C. Demir et al. https://doi.org/10.3390/su18157982
- Assessing the fidelity of multi-satellite precipitation estimates for drought monitoring in a mountain water tower to arid basin system M. Nikoo et al. https://doi.org/10.1016/j.jaridenv.2025.105519
- Hydrological insights: Comparative analysis of gridded potential evapotranspiration products for hydrological simulations and drought assessment M. Abdallah et al. https://doi.org/10.1016/j.ejrh.2024.102113
- Analyzing and predicting residential electricity consumption using smart meter data: A copula-based approach W. Softah et al. https://doi.org/10.1016/j.enbuild.2025.115432
- Enhancing precipitation merging accuracy in China with machine learning and rain-snow classification Q. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.135368
- The role of landscape in the formation of river flow in the Nakhchivan autonomous republic L. Ibrahimova & H. Imanov https://doi.org/10.26565/2410-7360-2025-62-14
- Machine learning approaches for enhanced estimation of reference evapotranspiration (ETo): a comparative evaluation A. Farag https://doi.org/10.1038/s41598-025-23166-w
8 citations as recorded by crossref.
- Evolution of precipitation-runoff-sediment dependence under environmental change: A Vine Copula analysis of two Loess Plateau basins J. Li et al. https://doi.org/10.1016/j.ejrh.2026.103505
- Bidirectional Extreme Response Analysis for a Synchronized-Period Evaluation of Multiple Precipitation Datasets C. Demir et al. https://doi.org/10.3390/su18157982
- Assessing the fidelity of multi-satellite precipitation estimates for drought monitoring in a mountain water tower to arid basin system M. Nikoo et al. https://doi.org/10.1016/j.jaridenv.2025.105519
- Hydrological insights: Comparative analysis of gridded potential evapotranspiration products for hydrological simulations and drought assessment M. Abdallah et al. https://doi.org/10.1016/j.ejrh.2024.102113
- Analyzing and predicting residential electricity consumption using smart meter data: A copula-based approach W. Softah et al. https://doi.org/10.1016/j.enbuild.2025.115432
- Enhancing precipitation merging accuracy in China with machine learning and rain-snow classification Q. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.135368
- The role of landscape in the formation of river flow in the Nakhchivan autonomous republic L. Ibrahimova & H. Imanov https://doi.org/10.26565/2410-7360-2025-62-14
- Machine learning approaches for enhanced estimation of reference evapotranspiration (ETo): a comparative evaluation A. Farag https://doi.org/10.1038/s41598-025-23166-w
Saved (final revised paper)
Latest update: 25 Aug 2026
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...