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Spatio-Temporal Changes in Rainfall Patterns and Their Implications for Water Resources in Eleyele Dam Basin

Authors

Solomon Ayobami Adefisoye

Department of Civil Engineering, Faculty of Engineering and Technology Lead City University, Ibadan, Nigeria (NG)

Olatunji Sunday Olaniyan

Department of Civil Engineering, Faculty of Engineering and Technology Ladoke Akintola University of Technology, Ogbomosho, Nigeria (NG)

Adedayo Ayodele Adegbola

Department of Civil Engineering, Faculty of Engineering and Technology Ladoke Akintola University of Technology, Ogbomosho, Nigeria (NG)

Article Information

DOI: 10.51583/IJLTEMAS.2025.1410000096

Subject Category: Environmental Engineering

Volume/Issue: 14/10 | Page No: 797-807

Publication Timeline

Submitted: 2025-11-14

Published: 2025-11-14

Abstract

Abstract: This study investigates the spatio-temporal variability and long-term rainfall trends in the Eleyele Dam Basin, southwestern Nigeria, using daily ERA5/ERA5-Land datasets covering 1981–2023. Preprocessing ensured homogeneity, persistence control, and serial correlation adjustment. Trend analysis was performed using the non-parametric Mann–Kendall test and Sen’s slope estimator, with trend-free prewhitening applied to mitigate autocorrelation effects. Spatial rainfall distribution was generated through Inverse Distance Weighting (IDW), optimized via leave-one-out cross-validation. Results indicate a persistent north–south gradient, with annual totals ranging from ~1,266 mm in the north to ~1,539 mm in the south. The basin exhibits a distinct bi-peak regime, with maxima in June and September, and 86% of rainfall concentrated between April and October. Statistically significant negative trends were identified in March, April, May, and December (Sen’s slope −0.48 to −1.62 mm yr⁻¹), pointing to a delayed onset and weakened early wet-season contribution. Annual rainfall shows a significant decline (−5.26 mm yr⁻¹), and dry-season totals also decreased (−2.91 mm yr⁻¹). Coefficients of variation highlight increased interannual variability, especially in dry months, underscoring unreliability of inflows. Spatial maxima occur in the southern sub-catchment, where high rainfall intensity coincides with greater erosion and sediment delivery risks. These shifts imply reduced inflows, prolonged dry-season deficits, and heightened supply shortfalls. The findings underscore the need for adaptive reservoir rule-curve updates, catchment erosion mitigation, and climate-resilient water resource planning to safeguard Eleyele’s multipurpose role in Ibadan.

Keywords

Rainfall variability; Spatio-temporal analysis, Mann–Kendall test, Sen’s slope estimator, Inverse Distance Weighting (IDW), Climate variability, Hydrological trends, Reservoir inflows, Water resource management, Eleyele Dam Basin

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References

1. Adejumo, A. (2023). Urban expansion and loss of watershed of Eleyele Dam, Ibadan, Nigeria. Journal of Infrastructure Development, 15(2), 45–60. [Google Scholar] [Crossref]

2. AJSE. (2025). Trend analysis of hydro-meteorological variables using Mann–Kendall test and Sen’s slope estimator. Academy Journal of Science and Engineering, 19(1), 55–68. https://www.ajol.info/index.php/ajse/article/view/291213 [Google Scholar] [Crossref]

3. Armijos, E., Espinoza, J. C., Crave, A., Cochonneau, G., & Vauchel, P. (2020). Rainfall control on Amazon sediment flux: Synthesis from 20 years of monitoring. Environmental Research Communications, 2(4), 041001. https://doi.org/10.1088/2515-7620/ab9003 [Google Scholar] [Crossref]

4. Awode, A. E., Adewumi, J. R., Obiora-Okeke, O., & others. (2025). Analysis of rainfall variability and extreme events in South-Western Nigeria: Implications for water resource management and climate resilience. Bulletin of Natural Resources Research. https://doi.org/10.1186/s42269-025-01324-4 [Google Scholar] [Crossref]

5. Azpurua, M., & Dos Ramos, K. (2010). A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude. Progress in Electromagnetics Research M, 14, 135–145. https://doi.org/10.2528/PIERM10083103 [Google Scholar] [Crossref]

6. De Araújo, J. C., Güntner, A., & Bronstert, A. (2014). Hydrosedimentological modelling in a semi-arid catchment in northeastern Brazil: Processes and interactions. Journal of Hydrology, 509, 354–366. https://doi.org/10.1016/j.jhydrol.2013.11.043 [Google Scholar] [Crossref]

7. Dike, V. N., Shimizu, M. H., Diallo, I., & Lin, Z. (2020). Projected changes in West African rainfall patterns under global warming scenarios. Climate Dynamics, 55(9), 2495–2513. https://doi.org/10.1007/s00382-020-05379-4 [Google Scholar] [Crossref]

8. Frontiers in Climate. (2025). Trend analysis of hydro-meteorological variables in the Mississippi River Basin using the Mann–Kendall test. Frontiers in Climate, 7, Article 1481926. https://doi.org/10.3389/fclim.2025.1481926 [Google Scholar] [Crossref]

9. Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The Climate Hazards InfraRed Precipitation with Station data (CHIRPS): A new environmental record for monitoring extremes. Scientific Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66 [Google Scholar] [Crossref]

10. Fuwape, I. A., & Ogunjo, S. T. (2018). Modeling rainfall season onset and cessation over West Africa: Implications for agriculture and water resources. arXiv preprint. https://arxiv.org/abs/1811.09677 [Google Scholar] [Crossref]

11. Hamed, K. H., & Rao, A. R. (1998). A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1–4), 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X [Google Scholar] [Crossref]

12. Ibebuchi, C. C., & Abu, I. O. (2023). Rainfall variability patterns in Nigeria during the rainy season. Scientific Reports, 13(1), Article 7888. https://doi.org/10.1038/s41598-023-34970-7 [Google Scholar] [Crossref]

13. Iwaponline. (2021). Trend analysis of selected hydro-meteorological variables using Mann–Kendall and Sen’s slope approaches. Journal of Water and Climate Change, 12(7), 3099–3115. https://doi.org/10.2166/wcc.2021.261 [Google Scholar] [Crossref]

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

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

16. Nicholson, S. E. (2018). Climate variability in Africa during the last two centuries. Theoretical and Applied Climatology, 131(3), 583–593. https://doi.org/10.1007/s00704-016-2061-x [Google Scholar] [Crossref]

17. Odekunle, T. O. (2010). An assessment of the influence of the inter-tropical discontinuity on inter-annual rainfall variability in Nigeria. Theoretical and Applied Climatology, 99(3–4), 365–374. https://doi.org/10.1007/s00704-009-0142-0 [Google Scholar] [Crossref]

18. Ogunjo, S. T., Ife-Adediran, T. S., & Fuwape, I. A. (2018). Quantification of historical drought conditions over different climatic zones of Nigeria. arXiv preprint. https://arxiv.org/abs/1810.00317 [Google Scholar] [Crossref]

19. Oyelakin, T. A., Okonofua, O. J., & Oguntunde, P. G. (2023). Remote evaluation of sedimentation of Eleyele Reservoir, Ibadan, Nigeria. Cambridge Journal of Environmental Research and Studies, 27(4), 123–135. [Google Scholar] [Crossref]

20. Pingale, S. M., Khare, D., Jat, M. K., & Adamowski, J. (2014). Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centres of the arid and semi-arid state of Rajasthan, India. Atmospheric Research, 138, 73–90. https://doi.org/10.1016/j.atmosres.2013.10.024 [Google Scholar] [Crossref]

21. Scanlon, B. R., Healy, R. W., & Cook, P. G. (2006). Groundwater recharge in semiarid and arid regions of the world: Present status and future prospects. Hydrogeology Journal, 14(3), 333–347. https://doi.org/10.1007/s10040-005-0433-1 [Google Scholar] [Crossref]

22. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934 [Google Scholar] [Crossref]

23. Tesemma, Z. K., Mohamed, Y. A., & Steenhuis, T. S. (2010). Trends in rainfall and runoff in the Blue Nile Basin: 1964–2003. Hydrological Processes, 24(25), 3747–3758. https://doi.org/10.1002/hyp.7893 [Google Scholar] [Crossref]

24. Tijani, M. N., Okunlola, O. A., & Ikpe, E. U. (2007). A geochemical assessment of water and bottom sediments contamination of Eleyele Lake Catchment, Ibadan, SW-Nigeria. European Journal of Scientific Research, 19(1), 105–120. [Google Scholar] [Crossref]

25. Yavuz, H., & Erdoğan, S. (2012). Spatial analysis of monthly and annual precipitation trends in Turkey. Water Resources Management, 26(3), 609–621. https://doi.org/10.1007/s11269-011-9935-2 [Google Scholar] [Crossref]

26. Yue, S., & Pilon, P. (2004). A comparison of the power of the t-test, Mann–Kendall and bootstrap for trend detection. Hydrological Sciences Journal, 49(1), 21–37. https://doi.org/10.1623/hysj.49.1.21.53996 [Google Scholar] [Crossref]

27. Yue, S., & Wang, C. Y. (2002). Applicability of prewhitening to eliminate the influence of serial correlation on the Mann–Kendall test. Water Resources Research, 38(6), 4-1–4-7. https://doi.org/10.1029/2001WR000861 [Google Scholar] [Crossref]

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