Abstract

The increasing frequency and impacts of flooding in the world have been blamed on global warming and climate change together with other anthropogenic factors. However some studies maintain the recent increase in flooding globally is mainly due to increasing extreme precipitation or rainfalls. In Ghana also, some researchers, technocrats and ordinary people believe that the recent increase in flooding in the city of Accra is resulting from increasing occurrence of extreme rainfall events, attributed to climate change. But this view was contested by other researchers who attributed the increasing flooding events purely to anthropogenic factors. This study, therefore, analyzed the temporal trend of extreme rainfall events from 1970 to 2009 to ascertain whether extreme rainfall events have been increasing significantly over the period under review in Ga West District to warrant the increasing flood events in the study area. A Manual Mann-Kendall Statistical Trend Test Table was used to analyze extreme 24hour maximum rainfall events which were extracted from secondary rainfall data procured from Ghana Meteorological Agency(GMet) for the Airport Weather Station as proxy data. Since the major raining season occurs from April to July in southern Ghana, the annual number of days of these extreme 24hour maximum rainfall events for the four months were extracted from the raw data for each year, from 1970 to 2009 for temporal trend analysis at the p=0.05 level of significance. The hypothesis of the study was as follows: i) Null hypothesis (H 0 ): there was no monotonic trend in the extreme 24-hour maximum rainfall events in the study area. ii) Alternative hypothesis (H 1 ): there was a monotonic trend in the extreme 24hour maximum rainfall events in the study area. The study found that Z=0.0058 which was less than Z (1- p / 2 ) = 1.96 at p=0.05 significant level. The null hypothesis (H 0 ) was, therefore accepted and the alternative hypothesis rejected. The acceptance of the null hypothesis shows that there was no statistically significant increase in extreme 24hour maximum rainfall events in the study area. The study, therefore, concluded that since there was no statistically significant increasing trend in extreme 24 hour maximum rainfall events, extreme rainfall events could not be the reason for the increasing flooding in the study area as some believed, but rather anthropogenic factors, or a combination of both. Keywords : Mann-Kendal statistical trend test, extreme 24hour maximum rainfall, anthropogenic factors, climate change, temporal analysis DOI: 10.7176/JEES/10-12-05 Publication date: December 31 st 2020

Highlights

  • Flood disasters are considered the most destructive of all natural disasters in terms of deaths, health hazards and damages to properties (Miller, 1997)

  • 2.0 Materials and Method 2.1 Study Area Ga West District was chosen as the study area through informal conversation with some key Officials of National Disaster Management Organization (NADMO) of Ghana, based on the fact that it is one of the relatively recent areas in Greater Accra Metropolitan Area (GAMA) that was suffering from flood disasters

  • It basically corresponds to the areas covered by the Accra Metropolitan assembly (AMA), the Ga East District Assembly (GEDA), the Ga West District Assembly (GWDA) and the Tema Municipal Assembly (TMA) (SWITCH Accra, 2009) which is coterminous with Accra (Brinkhoff, 2010)

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Summary

Introduction

Flood disasters are considered the most destructive of all natural disasters in terms of deaths, health hazards and damages to properties (Miller, 1997). Extreme 24hour maximum rainfall in this work is defined as a daily rainfall event with a threshold above 64mm according to key official of GMet. 2.3 Data Analysis Mann-Kendall Statistical Trend Test is a ranked based approach that consists of comparing each value of the time series with the remaining in a sequential order. The alternative hypothesis (H1) on the other hand is that there is a monotonic trend in the dataset This non-parametric Mann- Kendall trend test was applied to extreme 24hour maximum rainfall events extracted from rainfall data collected from GMet on KIASWS to investigate the temporal trend of extreme 24hour maximum rainfall events in the study area.

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