Research Article
Establishing Climate Change on Temperature Trend, Variation and Change Point Pattern in Warri, Nigeria
Kigigha Olali,
Ify Lawrence Nwaogazie*
,
Chiedozie Francis Ikebude
Issue:
Volume 13, Issue 2, June 2025
Pages:
90-101
Received:
28 February 2025
Accepted:
10 March 2025
Published:
26 March 2025
Abstract: The study aimed to establish climate change on temperature trends, variation and change point patterns in Warri, Nigeria, using 31-year daily temperature data (1992-2022). The primary data were obtained from the Nigerian Meteorological Agency (NIMET) for Warri to understand the temperature dynamic in the city. Both the annual maximum and minimum temperatures were extracted from the dataset and also the mean temperature was obtained by getting the mean temperature of the maximum temperature values. Mann-Kendall trend tests, linear regression, change point detection through CUSUM analysis, and Sequential Mann-Kendall tests were used for the trend and change point analyses. Results revealed statistically significant increasing trends in annual maximum temperature (0.02°C/year) and mean temperature (0.025°C/year), while minimum temperature showed a non-significant positive trend. Change point analysis identified significant shifts in maximum and mean temperatures around 2005-2006. The average annual maximum temperature was 36.35°C, with temperature yearly projections suggesting potential increases to nearly 40°C over the next century if current trends continue. These findings have important implications for urban infrastructure and industrial operations in Warri, particularly given its significance as a major oil and gas hub. The study provides crucial insights for climate adaptation planning in coastal industrial cities experiencing warming trends.
Abstract: The study aimed to establish climate change on temperature trends, variation and change point patterns in Warri, Nigeria, using 31-year daily temperature data (1992-2022). The primary data were obtained from the Nigerian Meteorological Agency (NIMET) for Warri to understand the temperature dynamic in the city. Both the annual maximum and minimum te...
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Review Article
Comparative Analyses of Stationary and Non-Stationary IDF Rainfall Models for Umuahia
Issue:
Volume 13, Issue 2, June 2025
Pages:
102-113
Received:
31 March 2025
Accepted:
9 April 2025
Published:
29 April 2025
DOI:
10.11648/j.hyd.20251302.12
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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.
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 d...
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