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Research Article |

General Extreme Value Fitted Rainfall Non-Stationary Intensity-Duration-Frequency (NS-IDF) Modelling for Establishing Climate Change in Benin City

The study focused on fitting non-stationary rainfall Intensity-Duration-Frequency (IDF) curves based on the General Extreme Value (GEV) distribution function to establish climate change existence in Benin City. The intensity levels were calculated, with the aid of the open-access R-studio software. Four linear behavioural parameter models considered for incorporating time as a covariate had the second model selected for producing the least corrected Akaike Information Criteria (AICC). The AICC varied from 370.30 to 125.20 for 15 and 1440 minutes, respectively, used in the calibration of the GEV equation. The computed non-stationary intensities produced higher values above those of stationary models, showing that the later IDF models undervalued extreme events. Differences of +15.24% (18.22 mm/hr), +9.4% (7.37 mm/hr), and +12.64% (12.78 mm/hr), for a 2, 10, and 50-year return periods, respectively, are serious underestimation from a stationary IDF model. Having extreme value differences could further aggravate the flood risk more than the design provision for the drainage facilities. The test statistic result confirmed a significant difference at a 95% confidence level between the non-stationary and stationary IDF curves, showing evidence of climatic change influenced by location as the time-variant parameter. The use of shorter-duration storms is advised for design purposes because they produce higher intensities and percentage differences in the extreme values, increasing the flood risk and infrastructural failures to induce climatic change in the study area.

Rainfall, Time Series Data, Trend, Non-Stationary, Stationary, Curve Fitting

APA Style

G. Sam, M., L. Nwaogazie, I., Ikebude, C. (2023). General Extreme Value Fitted Rainfall Non-Stationary Intensity-Duration-Frequency (NS-IDF) Modelling for Establishing Climate Change in Benin City. Hydrology, 11(4), 85-93. https://doi.org/10.11648/j.hyd.20231104.13

ACS Style

G. Sam, M.; L. Nwaogazie, I.; Ikebude, C. General Extreme Value Fitted Rainfall Non-Stationary Intensity-Duration-Frequency (NS-IDF) Modelling for Establishing Climate Change in Benin City. Hydrology. 2023, 11(4), 85-93. doi: 10.11648/j.hyd.20231104.13

AMA Style

G. Sam M, L. Nwaogazie I, Ikebude C. General Extreme Value Fitted Rainfall Non-Stationary Intensity-Duration-Frequency (NS-IDF) Modelling for Establishing Climate Change in Benin City. Hydrology. 2023;11(4):85-93. doi: 10.11648/j.hyd.20231104.13

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Ouarda, T. B. M. J., Yousef, L. A. and Charron, C. (2019) Non-stationary Intensity-Duration- Frequency Curves Integrating Information Concerning Tele-connections and Climatology https://doi.org/10.1002/joc.5953
2. Adamowski, K. and Bougadis, J. (2006) Detection of Trends in Annual Extreme Rainfall. Hydrological Processes, 17: 3547-3560. https://doi.org/10.1002/hyp.1353
3. Cheng, L. and AghaKouchak, A. (2014) Non-Stationarity Precipitation Intensity- Duration-Frequency Curves for Infrastructure Design in a Changing Climate. Science Reports, 4, Article No. 7093, 1-6. https://doi.org/10.1038/srep07093
4. Ganguli, P. and Coulibaly, P. (2017) Does Non-Stationary in Rainfall Require Non-Stationary Intensity-Duration Frequency Curves? Hydrology and Earth System Sciences, 21, 6461-6483. https://doi.org/10.5194/hess-21-6461-2017.
5. Aghakouchak, A., Ragno, E., Love, C. and Moftakhari, H. (2018) Projected Changes in California’s Precipitation Intensity-Duration-Frequency Curves. California’s Fourth Climate Change Assessment, California Energy Commission. Pub. No.: CCCA4-CEC-2018-005.
6. Ren, H., Hou, Z. J., Wigmosta, M., Liu, Y. and Leung, L. R. (2019) Impacts of Spatial Heterogeneity and Temporal Non-Stationarity of Intensity-Duration-Frequency Estimates – A Case Study in a Mountainous California-Nevada Watershed. Water, 11, 1296. https://doi.org/10.3390/w11061296
7. Sam, M. G., Nwaogazie, I. L and Ikebude, C. 2023a. Establishing Climatic Change on Rainfall Trend, Variation and Change Point Pattern in Benin City, Nigeria. International Journal of Environment and Climate Change, 13 (5): 202-212. https://doi.org /10.9734/IJECC/2023/v13i51761.
8. Sam, M. G., Nwaogazie, I. L., Ikebude, C., Iyang, U. J. and Irokwe, J. O. 2023b Modeling Rainfall Intensity-Duration-Frequency (IDF) and Establishing Climate Change Existence in Uyo-Nigeria Using Non-Stationary Approach. Journal of Water Resource and Protection, 15, 194-214. https://doi.org/10.4236/jwarp.2023.155012
9. Sam, M. G., Nwaogazie, I. L. and Ikebude, C. (2021). Improving Indian Meteorological Department Method for 24-hourly Rainfall Downscaling to Shorter Durations for IDF Modeling. International Journal of Hydrology, 5 (2), 72-82. https://doi.org/10.15406/ijh.2021.05.00268
10. Yue, S. and Wang, C. (2004) The Mann-Kendall Test-Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series. Water Resources Management, 18 (3), 201-218. https://doi.org/10.1023/B:WARM.0000043140.61082.60
11. Ahmad, I., Tango, D., Wang, T., Wang, M. and Wagan, B. (2015) Precipitation Trends over Time Using Mann- Kendall and Spearman’s Rho Tests in Swat River Basin, Pakistan, Advances in Meteorology, 2015, Article ID: 431860. https://doi.org/10.1155/2015/431860.
12. Okafor, G. C., Jimoh, O. D. and Larbi, K. I. (2017) Detecting Changes in Hydro-Climatic Variables during the Last Four Decades (1975-2014) on Downstream Kaduna River Catchment, Nigeria. Atmospheric and Climate Sciences, 7, 161-175. https://doi.org/10.4236/acs.2017.72012
13. Sam, M. G., Nwaogazie, I. L. and Ikebude, C. (2022). Climate Change and Trend Analysis of 24-Hourly Annual Maximum Series Using Mann-Kendall and Sen’s Slope Methods for Rainfall IDF Modeling. International Journal of Environment and Climate Change, 12 (2), 44-60. https://doi.org/10.9734/ijecc/2022/v12i230628
14. Cheng, L., AghaKoucak, A. Gilleland, E. and Katz, R. W. (2014) Non-Stationary Extreme Value Analysis in a Changing Climate. Climate Change, 127, 353-369. https://doi.org/10.1007/s10584-014-1254-5
15. Coles, S., Bawa, J., Trenner, L. and Dorazio, P. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer, London. https://doi.org/10.1007/978-1-4471- 3675-0
16. Renard, B., Sun, X. and Lang, M. (2013) Bayesian Methods for Non-Stationary Extreme Value Analysis. In Extremes in a Changing Climate; Springer: Dordrecht. The Netherlands, pp. 39-95. https://doi.org/10.1007/978-94-007-4479-0_3
17. Meehl, G. A. et al. (2000) An introduction on trends in Extreme Weather and Climate Events: Observations, Socioeconomic Impacts Terrestrial Ecological Impacts and Model Projections. B Am Meterol Soc. 81, 413-416. https://doi.org/10.1175/1520-0477(2000)081<0413:AITTIE>2.3.CO;2
18. Katz, R. (2010) Statistics of Extremes in Climate Change. Climate Change, 100, 71-76. https://doi.org/10.1007/s10584-010-9834-5
19. Gilleland, E. and Katz, R. W. (2011) New Software to Analyze How Extremes Change Over Time. EOS Trans. Am. Geophys. Union. 92, 13-14. https://doi.org/10.1029/2011EO020001
20. Silva, D. F., and Simonovic, S. P. (2020) Development of Non-Stationary Rainfall Intensity Duration Frequency Curves for Future Climate Conditions. Water Resources Research Report No: 106. Department of Civil and Environmental Engineering, Western University, Canada, 43 pages. ISBN (print) 978-0-7714-3137-1; (Online) 978-0-7714-3138-8.
21. Katz, R. W. et al. (2013) Statistical methods for nonstationary extremes. In: Kouchak, A. A., et al. (Eds.). Extremes in a Changing Climate: Detection, Analysis and Uncertainty. Springer, Dordrecht, pp. 15-37. https://doi.org/10.1007/978-94-007-4479-0_2
22. Sugahara, S., and Rocha, R. P. and Silveira, R. (2009) Non-stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil, 29, 1339–1349. https://doi.org/10.1002/joc.1760.
23. Cooley, D. (2013) Return Periods and Return Levels under Climate Change. Extremes in a Changing Climate, Springer, Netherlands. https://doi.org/10.1007/978-94-007-4479-0_4
24. Kothari, C. R. and Garg, G. (2014) Research Methodology: Methods and Techniques. 3rd ed. New Age International (P) Limited, Publishers, New Delhi.
25. RStudio Team (2020) RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.