variability of extreme wet events over malawi

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2017
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Abstract
Adverse effects of extreme wet events are well documented by several studies around the world. These effects are exacerbated in developing countries like Malawi that have insufficient risk reduction strategies and capacity to cope with extreme wet weather. Ardent monitoring of the variability of extreme wet events over Malawi is therefore imperative. The use of the Expert Team on Climate Change Detection and Indices (ETCCDI) has been recommended by many studies as an effective way of quantifying extreme wet events. In this study, ETCCDI indices were used to examine the number of heavy, very heavy, and extremely heavy rainfall days; daily and five-day maximum rainfall; very wet and extremely wet days; annual wet days and simple daily intensity. The Standard Normal Homogeneity Test (SNHT) was employed at 5% significance level before any statistical test was done. Trend analysis was done using the nonparametric Mann-Kendall statistical test. All stations were found to be homogeneous apart from Mimosa. Trend results show high temporal and spatial variability with the only significant results being: increase in daily maximum rainfall (Rx1day) over Karonga and Bvumbwe, increase in five-day maximum rainfall (Rx5day) over Bvumbwe. Mzimba and Chileka recorded a significant decrease in very wet days (R95p) while a significant increase was observed over Thyolo. Chileka was the only station which observed a significant trend (decrease) in extremely wet rainfall (R99p). Mzimba was the only station that reported a significant trend (decrease) in annual wet-day rainfall total (PRCPTOT) and Thyolo was the only station that reported a significant trend (increase) in simple daily intensity (SDII). Furthermore, the findings of this study revealed that, during wet years, Malawi is characterised by an anomalous convergence of strong south-easterly and north-easterly winds. This convergence is the main rain bringing mechanism to Malawi.
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brigadier2017geographicavariability Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Libanda Brigadier;Zheng Mie;Banda Noel
Journal international data privacy law
Year 2017
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