Trend Analysis and Time-Series Forecasting of Climate Variability in Urban and Rural Settlements of Niger State, Nigeria

Climate variability has become a major environmental concern due to its implications for water resources, agriculture, ecosystem sustainability, and human livelihoods. This study analysed historical climate trends and forecasted future climate variability in selected urban and rural settlements of Niger State, Nigeria, using statistical and time-series techniques. Annual rainfall and temperature data covering the period 1994–2024 were analysed using the Mann–Kendall trend test and Sen's Slope estimator, while future climate conditions were projected using Autoregressive Integrated Moving Average (ARIMA) models. The results revealed significant warming trends across the study area. Temperature increased by approximately 16.0°C in Mokwa and Muwo, 18.2°C in Minna and Garatu, 17.1°C in Kontagora, and 2.9°C in Rafin Gora between 1994 and 2024. Rainfall exhibited spatial variability, with substantial declines in some settlements and slight increases in others. Mann–Kendall and Sen's Slope analyses confirmed increasing temperature trends and varying rainfall patterns. The ARIMA model identification process indicated that ARIMA(1,2,1) was suitable for rainfall forecasting, while ARIMA(1,1,1) adequately modelled temperature variations. Diagnostic statistics confirmed the adequacy of the selected models, with non-significant Ljung–Box statistics indicating white-noise residuals.
Forecasts suggest continued temperature increases and increasing climate variability across both urban and rural settlements up to 2054. The study concludes that climate variability in Niger State is intensifying and may pose significant environmental and socio-economic challenges if adaptation measures are not implemented. The study recommends the integration of climate forecasting into regional planning, strengthening of climate monitoring systems, and implementation of adaptation strategies to enhance resilience.

Climate variability, trend analysis, Rainfall Trends; Temperature Trends; ARIMA, Mann–Kendall, Sen's Slope, rainfall forecasting, temperature forecasting.

1. Adelekan, I. O., Johnson, C. A., Manda, M. Z., Matyas, D., Mberu, B., Parnell, S., Satterthwaite, D., & Vivekananda, J. (2015). Disaster risk and its reduction: An agenda for urban Africa. International Development Planning Review, 37(1), 33–43. https://doi.org/10.3828/idpr.2015.4 [Google Scholar] [Crossref]

2. Adelekan, I. O., Johnson, C., Manda, M., Matyas, D., & Roberts, D. (2020). Climate change adaptation in Nigeria: A review of policy and practice. Climate and Development, 12(4), 295–307. [Google Scholar] [Crossref]

3. Agaj, T., Budka, A., Janicka, E., et al. (2024). Forecasting water level changes using ARIMA and ETS models for sustainable environmental management. Scientific Reports, 14, Article 22444. [Google Scholar] [Crossref]

4. Ayanlade, A., Radeny, M., Morton, J. F., & Muchaba, T. (2018). Rainfall variability and adaptation strategies in Nigeria. Climate Risk Management, 22, 45–59. [Google Scholar] [Crossref]

5. Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. Holden-Day. [Google Scholar] [Crossref]

6. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley. [Google Scholar] [Crossref]

7. Doblas-Reyes, F. J., García-Serrano, J., Lienert, F., Biescas, A. P., & Rodrigues, L. R. L. (2013). Seasonal climate predictability and forecasting: Status and prospects. Bulletin of the American Meteorological Society, 94(8), 1089–1100. https://doi.org/10.1175/BAMS-D-12-00027.1 [Google Scholar] [Crossref]

8. Elneel, L., Zitouni, M. S., Mukhtar, H., & Al-Ahmad, H. (2024). Examining sea levels forecasting using autoregressive and Prophet models. Scientific Reports, 14, Article 14337. [Google Scholar] [Crossref]

9. Fan, C. (2025). Global temperature anomaly forecast: A comparative analysis of ARIMA and ETS models. Advances in Economics, Management and Political Sciences, 144, 70–76. [Google Scholar] [Crossref]

10. Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E., Chatterjee, M., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S., Mastrandrea, P. R., & White, L. L. (Eds.). (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. [Google Scholar] [Crossref]

11. Food and Agriculture Organization of the United Nations. (2023). The state of food and agriculture 2023: Revealing the true cost of food to transform agrifood systems. FAO. [Google Scholar] [Crossref]

12. Gilbert, R. O. (1987). Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold. [Google Scholar] [Crossref]

13. Haines, A., & Ebi, K. (2019). The imperative for climate action to protect health. New England Journal of Medicine, 380(3), 263–273. https://doi.org/10.1056/NEJMra1807873 [Google Scholar] [Crossref]

14. Helsel, D. R., Hirsch, R. M., Ryberg, K. R., Archfield, S. A., & Gilroy, E. J. (2020). Statistical methods in water resources (Techniques and Methods, Book 4, Chapter A3). U.S. Geological Survey. https://doi.org/10.3133/tm4A3 [Google Scholar] [Crossref]

15. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. [Google Scholar] [Crossref]

16. Intergovernmental Panel on Climate Change. (2021). Climate change 2021: The physical science basis. Cambridge University Press. [Google Scholar] [Crossref]

17. Intergovernmental Panel on Climate Change. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge University Press. [Google Scholar] [Crossref]

18. Intergovernmental Panel on Climate Change. (2023). Climate change 2023: Synthesis report. Intergovernmental Panel on Climate Change. [Google Scholar] [Crossref]

19. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (2023). Assessment report on biodiversity and ecosystem services. IPBES. [Google Scholar] [Crossref]

20. Iyeme, E. E., Ekah, U. J., Njok, A. O., Agbo, E. P., & Offorson, G. C. (2024). Trend analysis of climate change across Nigeria: A Mann–Kendall and Sen's approach. Archives of Current Research International, 24(11), 450–467. [Google Scholar] [Crossref]

21. Kendall, M. G. (1975). Rank correlation methods (4th ed.). Griffin. [Google Scholar] [Crossref]

22. Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science, 333(6042), 616–620. https://doi.org/10.1126/science.1204531 [Google Scholar] [Crossref]

23. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. [Google Scholar] [Crossref]

24. Masson-Delmotte, V., Zhai, P., Pirani, A., et al. (2021). Climate change 2021: The physical science basis. Cambridge University Press. [Google Scholar] [Crossref]

25. Merryfield, W. J., Lee, W. S., Boer, G. J., Dunstone, N., Doblas-Reyes, F. J., & Smith, D. M. (2020). Current and emerging developments in seasonal climate prediction. Bulletin of the American Meteorological Society, 101(7), E869–E896. https://doi.org/10.1175/BAMS-D-19-0138.1 [Google Scholar] [Crossref]

26. National Bureau of Statistics. (2022). Population and urban growth statistics. Abuja, Nigeria. [Google Scholar] [Crossref]

27. Nicholson, S. E. (2018). The ITCZ and rainfall variability over West Africa. Climate Dynamics, 50, 3373–3391. [Google Scholar] [Crossref]

28. Niang, I., Ruppel, O. C., Abdrabo, M. A., et al. (2014). Africa. In C. B. Field et al. (Eds.), Climate change 2014: Impacts, adaptation, and vulnerability. Cambridge University Press. [Google Scholar] [Crossref]

29. Oguntunde, P. G., Lischeid, G., Abiodun, B. J., & Dietrich, O. (2020). Climate variability and temperature trends in Nigeria. Theoretical and Applied Climatology, 141, 105–120. [Google Scholar] [Crossref]

30. Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1–24. https://doi.org/10.1002/qj.49710845502 [Google Scholar] [Crossref]

31. Oyeniyi, A. S., Akinyemi, F. O., & Adewumi, T. O. (2025). Urban expansion and land surface temperature dynamics in medium-sized cities of southwestern Nigeria. Climate, 13(4), Article 68. [Google Scholar] [Crossref]

32. Parmesan, C., & Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421(6918), 37–42. https://doi.org/10.1038/nature01286 [Google Scholar] [Crossref]

33. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379–1389. [Google Scholar] [Crossref]

34. Şen, Z. (2024). Moving trend analysis methodology for hydro-meteorology time series dynamic assessment. Water Resources Management, 38, 4415–4429. [Google Scholar] [Crossref]

35. Smith, D. M., Eade, R., Dunstone, N. J., Fereday, D., Hermes, M., & Murphy, J. M. (2019). Robust skill in decadal climate predictions. Geophysical Research Letters, 46(10), 5449–5457. https://doi.org/10.1029/2019GL082559 [Google Scholar] [Crossref]

36. Sultan, B., & Gaetani, M. (2016). Agriculture in West Africa in the twenty-first century: Climate change and impacts scenarios, and potential for adaptation. Frontiers in Plant Science, 7, Article 1262. https://doi.org/10.3389/fpls.2016.01262 [Google Scholar] [Crossref]

37. Sylla, M. B., Nikiema, P. M., Gibba, P., Kebe, I., & Klutse, N. A. B. (2016). Climate change over West Africa. Current Climate Change Reports, 2, 193–205. [Google Scholar] [Crossref]

38. Trenberth, K. E., Fasullo, J. T., & Shepherd, T. G. (2014). Attribution of climate extreme events. Journal of Climate, 27(9), 3129–3144. https://doi.org/10.1175/JCLI-D-13-00500.1 [Google Scholar] [Crossref]

39. United Nations Educational, Scientific and Cultural Organization. (2024). United Nations world water development report 2024: Water for prosperity and peace. UNESCO. [Google Scholar] [Crossref]

40. United Nations Environment Programme. (2024). Adaptation gap report 2024: Come hell and high water. UNEP. [Google Scholar] [Crossref]

41. Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A., Reidy Liermann, C., & Davies, P. M. (2010). Global threats to human water security and river biodiversity. Nature, 467(7315), 555–561. https://doi.org/10.1038/nature09440 [Google Scholar] [Crossref]

42. World Meteorological Organization. (2017). WMO guidelines on the calculation of climate normals (WMO-No. 1203). World Meteorological Organization. [Google Scholar] [Crossref]

43. World Meteorological Organization. (2017). Climate information and monitoring services. World Meteorological Organization. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet