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.
| Published in | Hydrology (Volume 11, Issue 4) |
| DOI | 10.11648/j.hyd.20231104.13 |
| Page(s) | 85-93 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Rainfall, Time Series Data, Trend, Non-Stationary, Stationary, Curve Fitting
| [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/. |
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
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Download@article{10.11648/j.hyd.20231104.13,
author = {Masi G. Sam and Ify L. Nwaogazie and Chiedozie Ikebude},
title = {General Extreme Value Fitted Rainfall Non-Stationary Intensity-Duration-Frequency (NS-IDF) Modelling for Establishing Climate Change in Benin City},
journal = {Hydrology},
volume = {11},
number = {4},
pages = {85-93},
doi = {10.11648/j.hyd.20231104.13},
url = {https://doi.org/10.11648/j.hyd.20231104.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hyd.20231104.13},
abstract = {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.
},
year = {2023}
}
TY - JOUR T1 - General Extreme Value Fitted Rainfall Non-Stationary Intensity-Duration-Frequency (NS-IDF) Modelling for Establishing Climate Change in Benin City AU - Masi G. Sam AU - Ify L. Nwaogazie AU - Chiedozie Ikebude Y1 - 2023/11/17 PY - 2023 N1 - https://doi.org/10.11648/j.hyd.20231104.13 DO - 10.11648/j.hyd.20231104.13 T2 - Hydrology JF - Hydrology JO - Hydrology SP - 85 EP - 93 PB - Science Publishing Group SN - 2330-7617 UR - https://doi.org/10.11648/j.hyd.20231104.13 AB - 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. VL - 11 IS - 4 ER -
Department of Civil and Environmental Engineering, University of Port Harcourt, Port Harcourt, Nigeria
