Preprints
https://doi.org/10.5194/hess-2023-179
https://doi.org/10.5194/hess-2023-179
10 Aug 2023
 | 10 Aug 2023
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

A D-vine copula-based quantile regression towards merging satellite precipitation products over a rugged topography at the upper Tekeze Atbara Basin of the Nile Basin

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

Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resources management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-sourced satellite-based precipitation products (SPPs) with the suitable resolution provide alternative options in these data-scarce regions, typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine Copula-based Quantile Regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the Multivariate Linear (MLQR) and the Bayesian Model Averaging (BMAQ), respectively, and two traditional merging methods: the simple modeling average (SMA) and the one-outlier-removed average (OORA) using the descriptive and categorical statistics. The rugged topography region of the Upper Tekeze-Atbara Basin in Ethiopia was selected as the study region. The Results indicated that the precipitation data estimates with DVQR, MLQR, and BMAQ models and traditional merging methods outperformed the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over UTAB. The DVQR model substantially improved all the statistical metrics considered over BMAQ and MLQR models. However, DVQR model does not outperform BMAQ and MLQR models in the probability of detection (POD) and false alarm ratio (FAR), although it has the best frequency bias index (FBI) and critical success index (CSI) among all the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over rugged topography regions such as UTAB.

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

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-179', Anonymous Referee #1, 29 Aug 2023
    • AC1: 'Reply on RC1', Ke Zhang, 22 Oct 2023
  • RC2: 'Comment on hess-2023-179', Anonymous Referee #2, 20 Sep 2023
    • AC2: 'Reply on RC2', Ke Zhang, 22 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-179', Anonymous Referee #1, 29 Aug 2023
    • AC1: 'Reply on RC1', Ke Zhang, 22 Oct 2023
  • RC2: 'Comment on hess-2023-179', Anonymous Referee #2, 20 Sep 2023
    • AC2: 'Reply on RC2', Ke Zhang, 22 Oct 2023
Mohammed Abdallah, Ke Zhang, Lijun Chao, Abubaker Omer, Khalid Hassaballah, Kidane Welde Reda, Linxin Liu, Tolossa Lemma Tola, and Omar M. Nour
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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Short summary
A D-vine copula-based quantile regression (DVQR) is used for merging satellite precipitation products. The performance of the DVQR model is compared with simple modeling average and one-outlier-removed average methods. The nonlinear DVQR model outperforms the quantile regression-based Multivariate Linear and Bayesian Model Averaging.