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Rainfall erosivity estimation for Sierra Leone using non-parametric indices

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

Abstract

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.

Notes

Acknowledgements

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.

References

  1. Aguilar E, Peterson TC, Obando PR, Frutos R, Retana JA, Solera M, Soley J, García IG, Araujo RM, Santos AR, Valle VE, Brunet M, Aguilar L, Álvarez L, Bautista M, Castañón C, Herrera L, Ruano E, Sinay JJ, Sánchez E, Oviedo GIH, Obed F, Salgado JE, Vázquez JL, Baca M, Gutiérrez M, Centella C, Espinosa J, Martínez D, Olmedo B, Espinoza CEO, Núñez R, Haylock M, Benavides H, Mayorga R (2005) Changes in precipitation and temperature extremes in Central America and northern South America, 1961–2003. J Geophys Res 110:D23107.  https://doi.org/10.1029/2005JD006119 CrossRefGoogle Scholar
  2. Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J (2003) Guidelines on climate metadata and homogenization, WMO-TD No. 1186, 55 pp., World Meteorol. Org., Geneva, SwitzerlandGoogle Scholar
  3. Amara DMK, Oladele TA (2014) Estimation of soil erodibility and erosivity of rainfall patterns in Njala land resource area of southern Sierra Leone. Res J Agric Sci 5(2):163–167Google Scholar
  4. Angima SD, Scott DE, O’Neil MK, Weesies GA (2003) Soil erosion prediction using RUSLE for Central Kenya highland conditions. Agric Ecosyst Env 97(1/3):295–308CrossRefGoogle Scholar
  5. Angulo-Martínez M, Beguería S (2009) Estimating rainfall erosivity from daily precipitation records: a comparison among methods using data from the Ebro Basin (NE Spain). J Hydrol 379:111–121.  https://doi.org/10.1016/j.jhydrol.2009.09.051 CrossRefGoogle Scholar
  6. Arnoldus HMJ (1980) An approximation of the rainfall factor in the universal soil loss equation. In: De Boodt M, Gabriels D (eds) Assessment of erosion. John Wiley & Sons, Chichister, pp 127–132Google Scholar
  7. Bazzano MGP, Eltz FLF, Cassol EA (2010) Erosivity and hydrological characteristics of rainfalls in Rio Grande (RS, Brazil). Revista Brasileira de Ciência do Solo 34:235–244CrossRefGoogle Scholar
  8. Bertol I, Leite D, Engel FL, Cogo NP, González AP (2007) Erodibility of a Typic Hapludox evaluated under field conditions. Revista Brasileira de Ciência do Solo 31:541–549.  https://doi.org/10.1590/S0100-06832007000300014 CrossRefGoogle Scholar
  9. Bertol I, Zoldan Junior WA, Fabian EL, Zavaschi E, Pegoraro R, Gonzáles AP (2008) Effect of chiselling and rainfall erosivity on some characteristics of water erosion in a Nitosol under distinct management systems. Revista Brasileira de Ciência do Solo 32:747–757.  https://doi.org/10.1590/S0100-06832008000200027 CrossRefGoogle Scholar
  10. Bhalme HN (1991) El Nino-Southern Oscillation (ENSO) - Onset, growth and decay. The WMO/IMD Training Course in Monsoon Meteorology, Education and Training Programme, WMO/TD -No 496, 84–87.Google Scholar
  11. Bomah AK (1988) An analysis of the physical and land use variables affecting soil erosion of Sierra Leone, Doctoral Dissertation, Clark University, U.S.A.Google Scholar
  12. Capolongo D, Diodato N, Mannaerts CM, Piccarreta M, Strobl RO (2008) Analyzing temporal changes in climate erosivity using a simplified rainfall erosivity model in Basilicata (southern Italy). J Hydrol 356:119–130CrossRefGoogle Scholar
  13. Calvo JC (1998) Suspended sediment yield prediction models for Costa Rican watersheds. Hydrol Humid Tropic Environ 253:27–33Google Scholar
  14. Calvo-Alvarado JC, Gregory JD (1997). Predicting mean annual runoff and suspended sediment yield in rural watersheds in North Carolina. Water Resources Research Institute of the University of North Carolina Report 307Google Scholar
  15. De Longueville HYC, Kindo I, François GF, Ozerc P (2016) Long-term analysis of rainfall and temperature data in Burkina Faso (1950–2013). Int J Climatol.  https://doi.org/10.1002/joc.4640
  16. Essel P, Glover ET, Yeboah S, Adjei-Kyereme Y, Yawo IND, Nyarku M, Asumadu-Sakyi GS, Gbeddy GK, Agyiri YA, Ameho EM, Aberikae EA (2016) Rainfall erosivity index for the Ghana Atomic Energy Commission site. SpringerPlus 5:465.  https://doi.org/10.1186/s40064-016-2100-1 CrossRefGoogle Scholar
  17. GoSL (2007) National adaptation programme of action (NAPA). Final Report. Government of Sierra Leone. Ministry of Transport and aviation, Republic of Sierra Leone, West AfricaGoogle Scholar
  18. GoSL (2009) Second national communication on climate change. Government of Sierra Leone. Republic of Sierra Leone, West AfricaGoogle Scholar
  19. Fournier F (1960) Climat et érosion. La relation entre l'érosion du sol par l'eau et les précipitations atmosphériques. [Relationship between soil erosion by water and rainfall]. Presses Universitaires de France, Paris. (In French)Google Scholar
  20. Frazer-Williams RAD, Benjamin RGP, Kanu BD, Lahai SMG, Kanneh SM Jr., Dreiser C (2014) Environmental assessment and evaluation of natural disaster risk and mitigation in Freetown. Urban planning project 2011–2014, EuropeAid/128037/D/SER/SL-Cris. No.:FED/2010/250–190Google Scholar
  21. Hoyos N, Waylen PR, Jaramillo A (2005) Seasonal and spatial patterns of erosivity in a tropical watershed of the Colombian Andes. J Hydrol 314:177–191.  https://doi.org/10.1016/j.jhydrol.2005.03.014 CrossRefGoogle Scholar
  22. Kendall M (1975) Rank correlation measures. Charles Griffin, London, p 202Google Scholar
  23. Kinnell PIA (2010) Event soil loss, runoff and the universal soil loss equation family of models: a review. J Hydrol 385:384–397.  https://doi.org/10.1016/j.jhydrol.2010.01.024 CrossRefGoogle Scholar
  24. Konin TA (2015) Climate change adaptation strategies: water resources management in Senegal and Sierra Leone. Dissertation, Johns Hopkins University, Maryland, USAGoogle Scholar
  25. Lapworth DJ, MacDonald AM, Krishan G, Rao MS, Gooddy DC, Darling WG (2015) Groundwater recharge and age-depth profiles of intensively exploited groundwater resources in Northwest India. Geophys Res Lett 42:7554–7562.  https://doi.org/10.1002/2015GL065798. CrossRefGoogle Scholar
  26. Lee JH, Heo JH (2011) Evaluation of estimation methods for rainfall erosivity based on annual precipitation in Korea. J Hydrol 409(1–2):30–48CrossRefGoogle Scholar
  27. Lombardi Neto F, Moldenhauer WC (1992) Rainfall erosivity — its distribution and relationship with soil loss at Campinas, state of São Paulo, Brazil. Bragantia 51:189–196.  https://doi.org/10.1590/S0006-87051992000200008 CrossRefGoogle Scholar
  28. Mann HB (1945) Non-parametric tests against trend. Econometrica 33:245–259CrossRefGoogle Scholar
  29. McSweeney C, New M, Lizcano G (2010) UNDP climate change country profiles: Sierra Leone. UNDP. Retrieved on July 27, 2019 at http://country-profiles.geog.ox.ac.uk
  30. Mello CR, Sá MAC, Curi N, Mello JM, Viola MR, Silva AM (2007) Monthly and annual rainfall erosivity for Minas Gerais state. Pesq Agrop Brasileira 42:537–545.  https://doi.org/10.1590/S0100-204X2007000400011 CrossRefGoogle Scholar
  31. Meusburger K, Steel A, Panagos P, Montanarella L, Alewell C (2011) Spatial and temporal variability of rainfall erosivity factor for Switzerland. Hydrol Earth Syst Sci Discus 8:8291–8314.  https://doi.org/10.5194/hessd-8-8291-2011 CrossRefGoogle Scholar
  32. Mezősi G, Bata T (2016) Estimation of the changes in the rainfall erosivity in Hungary. J Environ Geog 9(3–4):43–48.  https://doi.org/10.1515/jengeo-2016-0011 CrossRefGoogle Scholar
  33. Nearing MA (2001) Potential changes in rainfall erosivity in the United States with climate change during the 21st century. J Soil Water Conservation 56:229–232Google Scholar
  34. Nearing MA, Jetten V, Baffaut C, Cerdan O, Couturier A, Hernandez M, le Bissonnais Y, Nichols MH, Nunes JP, Renschler CS, Souchère V, van Oost K (2005) Modelling response of soil erosion and runoff to changes in precipitation and cover. Catena 61:131–154.  https://doi.org/10.1016/j.catena.2005.03.007 CrossRefGoogle Scholar
  35. Oduro-Afriyie K (1996) Rainfall erosivity map for Ghana. Geoderma 74:161–166CrossRefGoogle Scholar
  36. Oliveira PTS, Alves ST, Rodrigues DBB, Panachuki E (2011a) Erosion risk mapping applied to environmental zoning. Water Resour Manag 25:1021–1036.  https://doi.org/10.1007/s11269-010-9739-0 CrossRefGoogle Scholar
  37. Oliveira PTS, Rodrigues DBB, Alves ST, Carvalho DF, Panachuki E (2011b) Spatial varibility of the rainfall erosive potencial in the State of Mato Grosso do Sul, Brazil, Brazil. Engenharia Agrícola 32:69–79.  https://doi.org/10.1590/S0100-69162012000100008 CrossRefGoogle Scholar
  38. Onchev NG (1985) Universal index for calculating rainfall erosivity. In: El-Swaify SA, Moldenhauer WC, Lo A (eds) Soil erosion and conservation. Soil Conservation Society of America, Ankeny, pp 424–431Google Scholar
  39. Ozer P, Mahamoud A (2013) Recent extreme precipitation and temperature changes in Djibouti City (1966–2011). J Climatol 2013:1–8.  https://doi.org/10.1155/2013/928501 CrossRefGoogle Scholar
  40. Pruski FF, Nearing MA (2002) Climate-induced changes in erosion during the 21st century for eight U.S. locations. Water Resour Res 38(12):1298.  https://doi.org/10.1029/2001WR000493 CrossRefGoogle Scholar
  41. Renard KG, Foster GR, Weesies GA, McCool DK (1996) Predicting soil erosion by water. A guide to conservation planning with the revised universal soil loss equation (RUSLE). Agric. Handbook 703.Google Scholar
  42. Renard KG, Freimund JR (1994) Using monthly precipitation data to estimate the R-factor in the revised USLE. J Hydrol 157:287–306.  https://doi.org/10.1016/0022-1694(94)90110-4 CrossRefGoogle Scholar
  43. Resilience Policy Team (2015) Sierra Leone climate action report. Resilience policy team, Irish Aid. Republic of Sierra Leone, West AfricaGoogle Scholar
  44. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389.  https://doi.org/10.1080/01621459.1968.10480934 CrossRefGoogle Scholar
  45. Silva AM (2004) Rainfall erosivity map for Brazil. Catena 57:251–259.  https://doi.org/10.1016/j.catena.2003.11.006 CrossRefGoogle Scholar
  46. Silva RB, Iori P, Silva FAM (2009) Proposition and compare of equations to estimate the rainfall erosivity in two cities of São Paulo state. Irrigation 14:533–547CrossRefGoogle Scholar
  47. Silva RB, Iori P, Armesto C, Bendini HN (2010) Assessing rainfall erosivity with artificial neural networks for the Ribeira Valley, Brazil. Int J Agron.  https://doi.org/10.1155/2010/365249
  48. Strangeways IC (1996) Back to basics: the ‘met. enclosure’: part 2(b) — raingauges, their errors. Weather 51:298–303CrossRefGoogle Scholar
  49. Tarawalli P (2012) Diagnostic analysis of climate change and disaster management in relation to the PRSP III in Sierra Leone. Sierra Leone, FreetownGoogle Scholar
  50. Trewin BC (2010) Exposure, instrumentation and observing practice effects on land temperature measurements. Wiley Interdis Rev: Clim Change 1:490–506Google Scholar
  51. USAID (2016) Climate change risk in Sierra Leone. Country Fact Sheet. pp. 1–5Google Scholar
  52. Vincent E et al (2011) Observed trends in indices of daily and extreme temperature and precipitation for the countries of the western Indian Ocean, 1961–2008. J Geophys Res 116:D10108.  https://doi.org/10.1029/2010JD015303 CrossRefGoogle Scholar
  53. Vincent LA, Wang XL, Milewska EJ, Wan H, Yang F, Swail V (2012) A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis. J Geophys Res 117:D18110.  https://doi.org/10.1029/2012JD017859 Google Scholar
  54. Wang XL (2008) Accounting for autocorrelation in detecting meanshifts in climate data series using the penalized maximal t or F test. J Appl Meteorol Climatol 47:2423–2444.  https://doi.org/10.1175/2008JAMC1741.1 CrossRefGoogle Scholar
  55. Wang XL, Feng Y (2009) RHtestsV3 user manual, report, 26 pp., Clim. Res. Div., Atmos. Sci. and Technol. Dir., Sci. and Technol. Branch, Environ. Canada, Gatineau, Quebec, Canada. Retrieved on July 20, 2019 at http://cccma.seos.uvic.ca/ETCCDI/software.shtml
  56. Wischmeier WH (1959) A rainfall erosion index for a universal soil- loss equation. Soil Sci Soc Am Proc 23(3):246–249.  https://doi.org/10.2136/sssaj1959.03615995002300030027x CrossRefGoogle Scholar
  57. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses. A guide to conservation planning. Agriculture Handbook No. 537. U.S. Department of Agriculture, Washington DC.Google Scholar
  58. World Bank (2017) Sierra Leone: rapid damage and loss assessment of August 14th, 2017 landslides and floods in the Western Area. http://documents.worldbank.org/curated/en/523671510297364577/Sierra-Leone-Rapid-damage-and-loss-assessment-of-August-14th-2017-landslides-and-floods-in-the-western-area. Accessed 25 November 2017.
  59. Zhai P, Zhang X, Wan H, Pan X (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18:1096–1108CrossRefGoogle Scholar

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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