Rainfall erosivity estimation for Sierra Leone using non-parametric indices

  • Denis Magnus Ken Amara
  • Kalim UllahEmail author
  • Zhou Yushu
Original Paper


Rainfall erosivity (R factor) has been determined for Sierra Leone using monthly precipitation data covering 1960–2013 using Fournier (FI) and modified Fournier (MFI) indices. Mann–Kendall, Pearson’s correlation and Sen’s slope tests were used to establish the trend in time series rainfall data and their correlation. The study showed that there is a decreasing trend in annual rainfall in all the districts and the average annual rainfall varies between 1400.7 and 3027 mm, with high variability in the southeast and western regions compared to northern region. Rainfall erosivity was in the low to extremely severe category ranging from 21.7 to 166.8 for Fournier index and 95.4 to 264.6 for modified Fournier index. The year 1998 recorded the highest erosivity with values ranging from severe to extremely severe. The rainfall aggressiveness varied from low to extremely severe. Trend analysis was significant for values of − 1.82 ≥ Z ≤ − 1.57 at the 99% and 95% confidence levels, respectively, with two-tailed tests. No significant change in the precipitation trend was observed for some districts at the 95% confidence level. However, a significant change was noted for Bo, Pujehun and Tonkolili districts at the 99% confidence level. Sen’s slope (Q) test revealed a non-significant decreasing slope magnitude for all 14 districts. Pearson’s correlation coefficients showed significant correlation between annual rainfall and erosivity but with stronger correlation for modified Fournier index than Fournier index. The decreasing rainfall trend and high erosivity may have challenging implications for natural resource management including vegetation, soil and water resources under current climate conditions.



The authors are grateful to COMSATS University Islamabad (former CIIT) and The World Academy of Sciences (TWAS) for the advancement of science in developing countries, and especially for the award of CIIT-TWAS Postdoctoral Fellowship through which this piece of work was possible. They are also thankful to Pakistan Science Foundation (PSF/NSFC-Earth/C-COMSATS-lsb (07)), National Natural Science Foundation of China (41661144024) and the Higher Education Commission of Pakistan (8035/Balochistan/NRPU/R&D/HEC/2017). They would like to thank HavestChoice for providing the valuable datasets that formed the basis for this research. The authors would also like to thank the anonymous reviewers and the editor for their valuable comments and suggestions to improve the quality of our manuscript.


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Denis Magnus Ken Amara
    • 1
    • 2
  • Kalim Ullah
    • 1
    Email author
  • Zhou Yushu
    • 3
  1. 1.Department of MeteorologyCOMSATS University Islamabad (CUI)IslamabadPakistan
  2. 2.Department of Soil Science, School of AgricultureNjala Campus, Njala UniversityFreetownSierra Leone
  3. 3.Key State Laboratory of Cloud-Precipitation Physics and Severe Storms (LACS), Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

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