Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia

Chimeme Martin Ekwueme; Ify Lawrence Nwaogazie; Chiedozie Francis Ikebude; Godwin Otunyo Amuchi; Jonathan Onyekachi Irokwe

doi:doi:10.11648/j.hyd.20251302.12

Review Article |

| Peer-Reviewed

Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia

Chimeme Martin Ekwueme

, Ify Lawrence Nwaogazie^*

, Chiedozie Francis Ikebude

, Godwin Otunyo Amuchi, Jonathan Onyekachi Irokwe

Published in Hydrology (Volume 13, Issue 2)

Received: 31 March 2025 Accepted: 9 April 2025 Published: 29 April 2025

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This study aimed to develop IDF models and compare rainfall intensity obtained from the stationary and non-stationary IDF models for Umuahia in South-Eastern Nigeria. The research used a long-term rainfall dataset spanning three decades (1992-2022) sourced from the Nigerian Meteorological Agency. The daily rainfall data recorded over 24 hours was downscaled to shorter periods using the Indian Meteorological Department model (IMD). For determining the best distribution fitting for the rainfall data, the Kolmogorov-Smirnov (K-S) test was utilised. The result from the K-S test revealed that Gumbel EVT-1 was the best-fitting distribution for creating the stationary IDF models. The GEVt-I model, which includes a time-dependent location parameter, proved the most effective for non-stationary models. The comparative analysis showed that non-stationary models forecasted greater rainfall intensities for shorter return periods (2-10 years), with variations between 4.93 and 16.16% for the 2-year return period. In contrast, for longer return periods (25-100 years), stationary models yielded higher intensity predictions, with differences ranging from -0.29 -13.21%. These results have important implications for infrastructure design and flood risk management in Umuahia, indicating that existing drainage systems based on stationary assumptions may be undersized by 5-16%, which could elevate the risk of flooding during typical rainfall events.

Published in	Hydrology (Volume 13, Issue 2)
DOI	10.11648/j.hyd.20251302.12
Page(s)	102-113
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), 2025. Published by Science Publishing Group

Keywords

Rainfall Intensity-Duration-Frequency (IDF), Non-stationary Modelling, Stationary Modelling, Climate Change, General Extreme Value (GEV) Distribution

References

[1]	Abiodun, B. J., Adegoke, J., Abatan, A. A., Ibe, C. A., Egbebiyi, T. S., Engelbrecht, F. & Pinto, I. (2017). Potential impacts of climate change on extreme precipitation over four African coastal cities. Climate change (2017) 143: 399-413. https://doi.org/10.1007/s10584-017-2001-5
[2]	AghaKouchak, A., Ragno, E., Love, C. & 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.
[3]	Akinsanola, A. A. & Ogunjobi, K. O. (2015). Recent homogeneity analysis and long-term spatio-temporal rainfall trends in Nigeria. Theoretical and Applied Climatology, 128, 275-289.
[4]	Cheng, L. & AghaKouchak, A. (2014). Non-stationarity precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Science Reports, 4(7093), 1-6.
[5]	Cheng, L., AghaKouchak, A., Gilleland, E. & Katz, R. W. (2014). Non-stationary extreme value analysis in a changing climate. Climate Change, 127(2), 353-369.
[6]	Coles, S., Bawa, J., Trenner, L. & Dorazio, P. (2001). An introduction to statistical modeling of extreme values. London: Springer.
[7]	Ekwueme, C. M., Nwaogazie, I. L., Ikebude, C. F., Amuchi, G. O., Irokwe, J. O., et al. (2025). Modeling Rainfall Intensity-Duration-Frequency (IDF) and Establishing Climate Change Existence in Umuahia - Nigeria Using Non-Stationary Approach. Hydrology, 13(1), 83-89. https://doi.org/10.11648/j.hyd.20251301.19
[8]	Ganguli, P. & Coulibaly, P. (2017). Does non-stationarity in rainfall require non-stationary intensity-duration-frequency curves? Hydrology and Earth System Sciences, 21, 6461-6483.
[9]	Katz, R. W. (2013). Statistical methods for nonstationary extremes. In: Extremes in a changing climate (pp. 15-37). Dordrecht: Springer.
[10]	Masi, M., Kanakoudis, V., & Salcedo, A. G. (2023). Statistical, Analytical and Numerical Approaches for the Design of Urban Drainage Systems under Climate Change Conditions. Climate, 11(7), 141. https://doi.org/10.3390/cli11070141
[11]	Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P. & Stouffer, R. J. (2008). Stationarity is dead: Whither water management? Science, 319(5863), 573-574. https://doi.org/10.1126/science.1151915
[12]	Nwaogazie, I. L., Sam, M. G., Enciso, R. Z., & Gonsalves, E. (2019). Probability and non-probability rainfall intensity-duration-frequency modeling for Port-Harcourt metropolis, Nigeria. International Journal of Hydrology, 3(1), 66-75.
[13]	Odjugo, P. A. O. (2013). Analysis of climate change awareness in Nigeria. Science Research Essays, 8(26), 1203-1211.
[14]	Ouarda, T. B. M. J., Yousef, L. A. & Charron, C. (2019). Non-stationary intensity-duration-frequency curves integrating information concerning teleconnections and climate change. International Journal of Climatology, 39, 2306-2323. https://doi.org/10.1002/joc.5953
[15]	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 modelling. International Journal of Hydrology; 5(2): 72-82. https://doi.org/10.15406/ijh.2021.05.00268
[16]	Sam, M. G., Nwaogazie, I. L., Ikebude, C., Inyang, U. J. and Irokwe, J. O. (2023a). Modeling Rainfall Intensity-Duration-Frequency (IDF) and Establishing Climate Change Existence in Uyo-Nigeria Using Non-Stationary Approach. Journal of water Resource and protection, Vol. 15, pp 194-214.
[17]	Sam, M. G., Nwaogazie, I. L., & Ikebude, C. (2023b). 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.
[18]	Sam, M. G., Nwaogazie, I. L., & Ikebude, C. (2023c). 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.
[19]	Silva, D. F. & 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.
[20]	Te Chow, V., Maidment, D. R., & Mays, L. W. (1988). Solutions Manual to Accompany Applied Hydrology. McGraw-Hill.
[21]	Xu, P., Wang, D., Wang, Y., Wu, J., Heng, Y., Singh, V. P.,... & Fang, H. (2024). Quantifying the urbanization and climate change-induced impact on changing patterns of rainfall Intensity-Duration-Frequency via nonstationary models. Urban Climate, 55, 101990. RetryClaude does not have internet access. Links provided may not be accurate or up to date.

Cite This Article

Plain Text BibTeX RIS

APA Style

Ekwueme, C. M., Nwaogazie, I. L., Ikebude, C. F., Amuchi, G. O., Irokwe, J. O. (2025). Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia. Hydrology, 13(2), 102-113. https://doi.org/10.11648/j.hyd.20251302.12

Copy | Download

ACS Style

Ekwueme, C. M.; Nwaogazie, I. L.; Ikebude, C. F.; Amuchi, G. O.; Irokwe, J. O. Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia. Hydrology. 2025, 13(2), 102-113. doi: 10.11648/j.hyd.20251302.12

Copy | Download

AMA Style

Ekwueme CM, Nwaogazie IL, Ikebude CF, Amuchi GO, Irokwe JO. Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia. Hydrology. 2025;13(2):102-113. doi: 10.11648/j.hyd.20251302.12

Copy | Download

@article{10.11648/j.hyd.20251302.12,
  author = {Chimeme Martin Ekwueme and Ify Lawrence Nwaogazie and Chiedozie Francis Ikebude and Godwin Otunyo Amuchi and Jonathan Onyekachi Irokwe},
  title = {Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia
},
  journal = {Hydrology},
  volume = {13},
  number = {2},
  pages = {102-113},
  doi = {10.11648/j.hyd.20251302.12},
  url = {https://doi.org/10.11648/j.hyd.20251302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hyd.20251302.12},
  abstract = {This study aimed to develop IDF models and compare rainfall intensity obtained from the stationary and non-stationary IDF models for Umuahia in South-Eastern Nigeria. The research used a long-term rainfall dataset spanning three decades (1992-2022) sourced from the Nigerian Meteorological Agency. The daily rainfall data recorded over 24 hours was downscaled to shorter periods using the Indian Meteorological Department model (IMD). For determining the best distribution fitting for the rainfall data, the Kolmogorov-Smirnov (K-S) test was utilised. The result from the K-S test revealed that Gumbel EVT-1 was the best-fitting distribution for creating the stationary IDF models. The GEVt-I model, which includes a time-dependent location parameter, proved the most effective for non-stationary models. The comparative analysis showed that non-stationary models forecasted greater rainfall intensities for shorter return periods (2-10 years), with variations between 4.93 and 16.16% for the 2-year return period. In contrast, for longer return periods (25-100 years), stationary models yielded higher intensity predictions, with differences ranging from -0.29 -13.21%. These results have important implications for infrastructure design and flood risk management in Umuahia, indicating that existing drainage systems based on stationary assumptions may be undersized by 5-16%, which could elevate the risk of flooding during typical rainfall events.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia

AU  - Chimeme Martin Ekwueme
AU  - Ify Lawrence Nwaogazie
AU  - Chiedozie Francis Ikebude
AU  - Godwin Otunyo Amuchi
AU  - Jonathan Onyekachi Irokwe
Y1  - 2025/04/29
PY  - 2025
N1  - https://doi.org/10.11648/j.hyd.20251302.12
DO  - 10.11648/j.hyd.20251302.12
T2  - Hydrology
JF  - Hydrology
JO  - Hydrology
SP  - 102
EP  - 113
PB  - Science Publishing Group
SN  - 2330-7617
UR  - https://doi.org/10.11648/j.hyd.20251302.12
AB  - This study aimed to develop IDF models and compare rainfall intensity obtained from the stationary and non-stationary IDF models for Umuahia in South-Eastern Nigeria. The research used a long-term rainfall dataset spanning three decades (1992-2022) sourced from the Nigerian Meteorological Agency. The daily rainfall data recorded over 24 hours was downscaled to shorter periods using the Indian Meteorological Department model (IMD). For determining the best distribution fitting for the rainfall data, the Kolmogorov-Smirnov (K-S) test was utilised. The result from the K-S test revealed that Gumbel EVT-1 was the best-fitting distribution for creating the stationary IDF models. The GEVt-I model, which includes a time-dependent location parameter, proved the most effective for non-stationary models. The comparative analysis showed that non-stationary models forecasted greater rainfall intensities for shorter return periods (2-10 years), with variations between 4.93 and 16.16% for the 2-year return period. In contrast, for longer return periods (25-100 years), stationary models yielded higher intensity predictions, with differences ranging from -0.29 -13.21%. These results have important implications for infrastructure design and flood risk management in Umuahia, indicating that existing drainage systems based on stationary assumptions may be undersized by 5-16%, which could elevate the risk of flooding during typical rainfall events.

VL  - 13
IS  - 2
ER  -

Copy | Download

Author Information

Chimeme Martin Ekwueme

Department of Civil and Environmental Engineering, University of Calabar, Calabar, Nigeria

http://orcid.org/0009-0008-0168-0251
Ify Lawrence Nwaogazie

Department of Civil and Environmental Engineering, University of Port Harcourt, Port Harcourt, Nigeria

Contact Email

http://orcid.org/0000-0001-9480-1658
Chiedozie Francis Ikebude

Department of Civil and Environmental Engineering, University of Port Harcourt, Port Harcourt, Nigeria

http://orcid.org/0009-0006-7579-0950
Godwin Otunyo Amuchi

Department of Civil and Environmental Engineering, University of Port Harcourt, Port Harcourt, Nigeria
Jonathan Onyekachi Irokwe

Department of Civil and Environmental Engineering, University of Port Harcourt, Port Harcourt, Nigeria

http://orcid.org/0009-0001-7718-4915

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Ekwueme, C. M., Nwaogazie, I. L., Ikebude, C. F., Amuchi, G. O., Irokwe, J. O. (2025). Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia. Hydrology, 13(2), 102-113. https://doi.org/10.11648/j.hyd.20251302.12

Copy | Download

ACS Style

Ekwueme, C. M.; Nwaogazie, I. L.; Ikebude, C. F.; Amuchi, G. O.; Irokwe, J. O. Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia. Hydrology. 2025, 13(2), 102-113. doi: 10.11648/j.hyd.20251302.12

Copy | Download

AMA Style

Ekwueme CM, Nwaogazie IL, Ikebude CF, Amuchi GO, Irokwe JO. Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia. Hydrology. 2025;13(2):102-113. doi: 10.11648/j.hyd.20251302.12

Copy | Download

@article{10.11648/j.hyd.20251302.12,
  author = {Chimeme Martin Ekwueme and Ify Lawrence Nwaogazie and Chiedozie Francis Ikebude and Godwin Otunyo Amuchi and Jonathan Onyekachi Irokwe},
  title = {Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia
},
  journal = {Hydrology},
  volume = {13},
  number = {2},
  pages = {102-113},
  doi = {10.11648/j.hyd.20251302.12},
  url = {https://doi.org/10.11648/j.hyd.20251302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hyd.20251302.12},
  abstract = {This study aimed to develop IDF models and compare rainfall intensity obtained from the stationary and non-stationary IDF models for Umuahia in South-Eastern Nigeria. The research used a long-term rainfall dataset spanning three decades (1992-2022) sourced from the Nigerian Meteorological Agency. The daily rainfall data recorded over 24 hours was downscaled to shorter periods using the Indian Meteorological Department model (IMD). For determining the best distribution fitting for the rainfall data, the Kolmogorov-Smirnov (K-S) test was utilised. The result from the K-S test revealed that Gumbel EVT-1 was the best-fitting distribution for creating the stationary IDF models. The GEVt-I model, which includes a time-dependent location parameter, proved the most effective for non-stationary models. The comparative analysis showed that non-stationary models forecasted greater rainfall intensities for shorter return periods (2-10 years), with variations between 4.93 and 16.16% for the 2-year return period. In contrast, for longer return periods (25-100 years), stationary models yielded higher intensity predictions, with differences ranging from -0.29 -13.21%. These results have important implications for infrastructure design and flood risk management in Umuahia, indicating that existing drainage systems based on stationary assumptions may be undersized by 5-16%, which could elevate the risk of flooding during typical rainfall events.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia

AU  - Chimeme Martin Ekwueme
AU  - Ify Lawrence Nwaogazie
AU  - Chiedozie Francis Ikebude
AU  - Godwin Otunyo Amuchi
AU  - Jonathan Onyekachi Irokwe
Y1  - 2025/04/29
PY  - 2025
N1  - https://doi.org/10.11648/j.hyd.20251302.12
DO  - 10.11648/j.hyd.20251302.12
T2  - Hydrology
JF  - Hydrology
JO  - Hydrology
SP  - 102
EP  - 113
PB  - Science Publishing Group
SN  - 2330-7617
UR  - https://doi.org/10.11648/j.hyd.20251302.12
AB  - This study aimed to develop IDF models and compare rainfall intensity obtained from the stationary and non-stationary IDF models for Umuahia in South-Eastern Nigeria. The research used a long-term rainfall dataset spanning three decades (1992-2022) sourced from the Nigerian Meteorological Agency. The daily rainfall data recorded over 24 hours was downscaled to shorter periods using the Indian Meteorological Department model (IMD). For determining the best distribution fitting for the rainfall data, the Kolmogorov-Smirnov (K-S) test was utilised. The result from the K-S test revealed that Gumbel EVT-1 was the best-fitting distribution for creating the stationary IDF models. The GEVt-I model, which includes a time-dependent location parameter, proved the most effective for non-stationary models. The comparative analysis showed that non-stationary models forecasted greater rainfall intensities for shorter return periods (2-10 years), with variations between 4.93 and 16.16% for the 2-year return period. In contrast, for longer return periods (25-100 years), stationary models yielded higher intensity predictions, with differences ranging from -0.29 -13.21%. These results have important implications for infrastructure design and flood risk management in Umuahia, indicating that existing drainage systems based on stationary assumptions may be undersized by 5-16%, which could elevate the risk of flooding during typical rainfall events.

VL  - 13
IS  - 2
ER  -

Copy | Download