Climate Change and Trend Analysis of 24-Hourly Annual Maximum Series Using Mann-Kendall and Sen Slope Methods for Rainfall IDF Modeling

Abstract

The aim of this study is the establishment of the existence of trend and variability on a typical 24-hourly sorted thirty years (1986-2015) annual maximum series (AMS) and maximum monthly series (MMS) rainfall data for Uyo metropolis in Nigeria. Data were downscaled into shorter durations of 0.25, 0.5, …,12 hours. The statistical tool applied for the study was the Mann-Kendall (MK) test and Sen Slope estimator. The results showed that there exists increasing trend for all durations analyzed with consistency in the test statistic results. The MK statistic lZl for the AMS varied between 3.1701 and 3.2827 while that of MMS was 4.756, were greater than critical Z = 1.96. Also, the computed p-value for the AMS varied between 0.0012 and 0.0015, and were lower than the significant level of alpha, = 0.05. Thus, the null hypothesis of no trend was rejected. Similarly, the Sen Slope estimator gave an average rate of change in rainfall as 2.1288 and 2.16 mm/year for AMS and MMS time series data, respectively. The result from the Sen Slope estimator indicated that the magnitude of the trend decreased as the duration of rainfall increased such that shorter duration exhibited more trend than higher duration. The results of the MK trend and Sen Slope analysis proved that both test exhibited high degree of consistency with statistically significant positive trend and variability. These results have provided further evidence of an accelerated alarming rate in climate change increasing trend in Uyo metropolis and perhaps the environs. Therefore, planning for effective and accurate rainfall prediction for annual maximum time series data with established variability in trend will require adoption of non-stationary concept to account for the influence of changing climatic parameters in intensity-duration-frequency (IDF) modeling.