Hydrological evaluation of PERSIANN-CDR rainfall over upper Senegal River and Bani River Basins

Khalidou M. Bâ*, Luis Balcázar, Vitali Diaz, Febe Ortiz, Miguel A. Gómez-Albores, Carlos Díaz-Delgado

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)
145 Downloads (Pure)


This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash-Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R 2 ). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R 2 > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.

Original languageEnglish
Article number1884
Number of pages19
JournalRemote Sensing
Issue number12
Publication statusPublished - 2018

Bibliographical note

Special Issue "Remote Sensing of Water Resources in Semi-Arid Regions/Drought Areas"


  • Bani River
  • Discharge
  • ERA-Interim
  • Senegal River


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