Impact of Climate Change on Growing Season in Nigeria: Seasonal Rainfall Prediction (SRP) as Assessment and Adaptation Tool

  • Paul Akeh Ugbah
  • Olumide OlaniyanEmail author
  • Sabastine Dekaa Francis
  • Adamu James
Living reference work entry


Information on climate over many years shows signals of a changing climate in Nigeria and generally on a global scale caused by temperature increase. Climate change effects are increasing, making it necessary to appropriate actions informed by sound climate knowledge to mitigate the effect of these changes. This is achievable by integrating knowledge of climate change into local, national, and global policy processes. This chapter presents an analysis of temperature and rainfall data collected from 41 synoptic stations of the Nigerian Meteorological Agency (NiMet) spread across Nigeria between 1981 and 2017. It also looks at the effectiveness of the Nigerian Meteorological Agency’s Seasonal Rainfall prediction (SRP) as a climate Smart Agricultural tool to mitigate and adapt to the effects of climate change in Nigeria. The analysis showed that there is an increasing trend in annual mean maximum and minimum temperature anomalies. There has been a persistent increase in the maximum temperature anomalies especially in the past five (5) consecutive years (2013–2017). Rainfall analysis of recent years depicts positive standardized anomalies of above 0.5. An assessment of the SRP tool showed improvement and increase in skill of performance of the model in predicting seasonal rainfall onset, cessation, length of season, and annual rainfall amount across the country from 2012 to 2015, a period for which data was available. The skill was determined by calculating the percentage of forecast accuracy at 95% confidence level for 41 stations across the country. The analysis also shows that NiMet SRP could predict onset and cessation dates of rainfall with a skill above 70%, followed by length of season at a skill of 60% and the lowest skill of 51% in prediction of rainfall amount. The correlation between forecast and observed is 90% for all the rainfall parameters. The good skills suggest that NiMet SRP could serve as a useful adaptation tool to mitigate the effect of climate change on Agriculture over Nigeria and therefore recommended for use by stakeholders.


Climate change Climate smart Agriculture NiMet SRP Onset Cessation Length of season Rainfall amount 


  1. Arslan A, McCarthy N, Lipper L, Asfaw S, Cathaneo A, Kokwe M (2015) Climate smart agriculture? Assessing the adaptation implications in Zambia. J Agric Econ. Paper presented at the international conference of agricultural economics, Milan, 8–14 August.
  2. Ati OF, Stigter CJ, Oladipo EO (2002) A comparison of methods to determine the onset of the growing season in Northern Nigeria. Int J Climatol 22:731–742CrossRefGoogle Scholar
  3. Ayanlade A, Adeoye NO, Babatimehin O (2013) Intra-annual climate variability and malaria transmission in Nigeria. Bull Geogr Socio-Econ Ser 21(21):7–19Google Scholar
  4. Buba AD (2004) Climate change and water problems in Chad Republic. J Arid Environ 3(2):24–27Google Scholar
  5. CERC (2016) Cambridge Environmental Research Consultants (CERC) Ltd. Model evaluation toolkit user guide.
  6. Dunning CM, Black ECL, Allan RP (2016) The onset and cessation of seasonal rainfall over Africa. J Geophys Res Atmos 121:11405–11424CrossRefGoogle Scholar
  7. Eludoyin OM, Adelekan IO (2013) The physiologic climate of Nigeria. Int J Biometeorol 57:241–264CrossRefGoogle Scholar
  8. Eludoyin OM et al (2014) Air temperature, relative humidity, climate regionalization and thermal comfort of Nigeria. Int J Climatol 34:2000–2018. https://rmets.onlinelibrary. Scholar
  9. Embassy of Nigeria (2018) The Embassy of Nigeria in United States of America: History of Nigeria. Accessed in 2018
  10. FAO (2010) “Climate-smart” agriculture: policies, practices, and financing for food security, adaptation and mitigation. Food and Agriculture Organization of the United Nations, Rome. Scholar
  11. FAO (2013) Climate-smart agriculture: sourcebook. Food and agriculture organization of the United Nations, Rome. Scholar
  12. Farauta BK, Egbale CL, Idrissa YC, Agu VC (2011) Climate change and adaptation measures in northern Nigeria: Empherical situation and policy implications (No. 62). Nairobi, Kenya: African Technology Policy Studies Network. Accessed in 2018
  13. IPCC (2015) Climate change 2014: synthesis report. In: Core Writing Team, Pachauri RK, Meyer LA (eds) Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva, 151 ppGoogle Scholar
  14. Ismaila U, Gana AS, Tswanya NM, Dogara D (2010) Cereals production in Nigeria: constrainsts and opportunities for betterment. Afr J Agric Res 5(12):1341–1350Google Scholar
  15. Lipper L et al (2014) Climate-smart agriculture for food security. Nat Clim Chang 4:1068–1072CrossRefGoogle Scholar
  16. Matthew OJ, Imasogie OG, Ayoola MA, Abiye OE, Sunmonu LA (2017) Assessment of prediction schemes for estimating rainfall onset over different climatic zones in West Africa. J Geogr Environ Earth Sci Int 9(1):1–15CrossRefGoogle Scholar
  17. Mugalavai EM, Kikporir EC, Raes D, Rao MS (2008) Analysis of rainfall onset, cessation and length of growing season for western Kenya. J Agric For Meteorol 148:1123–1135CrossRefGoogle Scholar
  18. Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241CrossRefGoogle Scholar
  19. Legates DR, McCabe GJ (2013) Short communication. A refined index of model performance: a rejoinder. Int J Climatol 33:1053–1056CrossRefGoogle Scholar
  20. Neufeldt H, Kristjanson P, Thorlakson T, Gassner A, Norton-Griffiths M, Place F, Langford K (2011) Making climate-smart agriculture work for the poor. Policy brief, vol 12. World Agroforestry Centre (ICRAF), NairobiGoogle Scholar
  21. NiMet (2013) Nigerian meteorological agency: seasonal rainfall prediction brochure. Accessed Feb 2018
  22. NiMet (2014) Nigerian meteorological agency: seasonal rainfall prediction brochure. Accessed Feb 2018
  23. Nwanze KF, Fan S (2016) Climate change and agriculture: strengthening the role of small holders. Global food policy report. International Food Policy Research Institute, Washington, DC, pp 13–21Google Scholar
  24. Odekunle TO (2004) Rainfall and length of growing season in Nigeria. Int J Climatol 24:467–479CrossRefGoogle Scholar
  25. Odekunle TO, Balogun EE, Ogunkoya OO (2005) On the prediction of rainfall onset and retreat dates in Nigeria. Theor Appl Climatol 81:101–112CrossRefGoogle Scholar
  26. Odekunle TO, Oniarah A, Aremu SO (2008) Towards a wetter Sudano-Sahelian ecological zone in twenty-first century Nigeria. J Roy Meteorol Soc Weather 63(3):66–70Google Scholar
  27. Odjugo PAO (2007) The impact of climate change on water resources; global and regional analysis. Indones J Geogr 39:23–41Google Scholar
  28. Olawale EO, Isaac KT, Labode P (2016) Differential impacts of rainfall and irrigation on agricultural production in Nigeria: any lessons for climate-smart agriculture? Elsevier B.V. Agric Water Manag 178(2016):30–36Google Scholar
  29. Omotosho JA, Kerandi NM (2008) Seasonal rainfall prediction in Kenya using empirical methods. J Kenya Meteorol Soc 2(2):114–124Google Scholar
  30. Omotosho BJ, Balogun AA, Ogunjobi K (2000) Predicting monthly and seasonal rainfall, onset and cessation of the rainy season in West Africa using only surface data. Int J Climatol 20:865–880CrossRefGoogle Scholar
  31. Quan X, Hoerling M, Whitaker J, Bates G, Xu T (2006) Diagnosing sources of U.S. seasonal forecast skill. J Clim 19:3279–3293. Scholar
  32. Terdoo F, Adekola O (2014) Assessing the role of climate-smart agriculture in combating climate change, desertification and improving rural livelihood in Northern Nigeria. Afr J Agric Res 9(15):1180–1191CrossRefGoogle Scholar
  33. Verheye W (2010) Growth and production of maize: traditional low-input cultivation. In: Verheye W (ed) Land use, land cover and soil sciences. Encyclopaedia of Life Support Systems (EOLSS), UNESCO-EOLSS Publishers, Oxford. http://www.eolss.netGoogle Scholar
  34. Wanders N, Wood EF (2016) Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations. Environ Res Lett 11(9):4007CrossRefGoogle Scholar
  35. Willmott CJ et al (2012) Short communication: a refined index of model performance. Int J Climatol 32:2088–2094. Scholar
  36. Zoellick RB (2009) A climate smart future. The Nation Newspapers. Vintage Press Limited, Lagos, p 18Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paul Akeh Ugbah
    • 1
  • Olumide Olaniyan
    • 1
    Email author
  • Sabastine Dekaa Francis
    • 1
  • Adamu James
    • 1
  1. 1.National Weather Forecasting and Climate Research Centre, Nigerian Meteorological AgencyAbujaNigeria

Personalised recommendations