Browsing by Author "Habeeb Oladimeji Ganiyu, Faridah Othman, Wan Zurina Wan Jaafar and Cia Yik Ng1"

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    Comprehensive evaluation of satellite precipitation products over sparsely gauged river basin in Nigeria
    (Springer Nature, 2025-02-20) Habeeb Oladimeji Ganiyu, Faridah Othman, Wan Zurina Wan Jaafar and Cia Yik Ng1
    The study evaluated GPM-IMERG07, CHIRPS2.0, CPC-CMORPH, and PERSIANN-CDR against ground observations of rainfall for five gauging stations over 10 years (2013–2022) in the Niger Central Hydrological Area, Nigeria. This area is prone to severe annual floods, which lead to devastating downstream effects. The lack of high density and evenly distributed rain gauge stations has hindered effective research on mitigating food impacts, necessitating alternative rainfall data sources. Satellite precipitation products (SPPs) are used globally because of their high temporal and spatial resolution, free accessibility, and extensive coverage. However, these products have inherent biases and require comprehensive evaluation. The study employed scatter plots and descriptive statistics for daily and monthly comparisons. Daily SPPs were further analyzed using categorical statistics and a four-component error decomposition method. The findings revealed a higher correlation and greater errors at the monthly temporal resolution than at the daily resolution. PERSIANN-CDR performed better (daily and monthly) with a slightly higher median correlation (0.33 and 0.86), RMSE (9.66 mm and 57.59 mm), and Bias (-0.13 and 4.02). Furthermore, it demonstrated exceptional rainfall detection probability (POD =85%) and minimal miss bias (MB=119.2 mm/day) compared to other SPPs. Therefore, the high performance of PERSIANN-CDR in most statistical indices validates its suitability for the study area. Nonetheless, SPPs exhibited high bias values above the perfect value of zero (0). Thus, it is recommended that algorithm developers reduce these biases to improve the suitability of SPPs. End users should also consider bias correction or data merging to enhance the accuracy of the hydrological simulations.