2020OALibOpen access

Spatio-Temporal Patterns of Rainfall Variability for Wet Season over Togo in West Africa

Pouwèréou Nimon, Issaou Latifou, Yawo Konko, Kouami Kokou

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Abstract

Climate change is affecting rainfall variability.This paper investigated the June-September (JJAS) rainfall variability using reanalyzed and observed datasets from 1976 to 2015 in Togo.The rotated empirical orthogonal function (REOF) method was used to get the distribution patterns of JJAS rainfall.The Mann-Kendall (MK) statistic was also used to detect temporal trend of the rotated principal component time series (RPCs) that represent the modes of positive loadings.The REOF method has revealed four significant patterns that explained 65.1% of the total variance; the first, the second and the fourth REOF modes exhibit mainly positive loadings, whereas the third exhibits negative loadings.The first mode (REOF1) represents mainly the southern part of Togo; the second mode (REOF2) represents the northern part, the third mode (REOF3) represents the western part and the fourth (REOF4) represents the north-eastern part of Togo.The Mann-Kendall test has revealed an increasing and significant trend of rainfall in the northern region of Togo.In contrast, the trends were not significant in the southern and north-eastern parts of the country.These results form a basis on which adaptation strategies may be taken in this region with high rainfall variability.

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Climate change is affecting rainfall variability.This paper investigated the June-September (JJAS) rainfall variability using reanalyzed and observed datasets from 1976 to 2015 in Togo.The rotated empirical orthogonal function (REOF) method was used to get the distribution patterns of JJAS rainfall.The Mann-Kendall (MK) statistic was also used to detect temporal trend of the rotated principal component time series (RPCs) that represent the modes of positive loadings.The REOF method has revealed four significant patterns that explained 65.1% of the total variance; the first, the second and the fourth REOF modes exhibit mainly positive loadings, whereas the third exhibits negative loadings.The first mode (REOF1) represents mainly the southern part of Togo; the second mode (REOF2) represents the northern part, the third mode (REOF3) represents the western part and the fourth (REOF4) represents the north-eastern part of Togo.The Mann-Kendall test has revealed an increasing and significant trend of rainfall in the northern region of Togo.In contrast, the trends were not significant in the southern and north-eastern parts of the country.These results form a basis on which adaptation strategies may be taken in this region with high rainfall variability.

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Available abstract

Climate change is affecting rainfall variability.This paper investigated the June-September (JJAS) rainfall variability using reanalyzed and observed datasets from 1976 to 2015 in Togo.The rotated empirical orthogonal function (REOF) method was used to get the distribution patterns of JJAS rainfall.The Mann-Kendall (MK) statistic was also used to detect temporal trend of the rotated principal component time series (RPCs) that represent the modes of positive loadings.The REOF method has revealed four significant patterns that explained 65.1% of the total variance; the first, the second and the fourth REOF modes exhibit mainly positive loadings, whereas the third exhibits negative loadings.The first mode (REOF1) represents mainly the southern part of Togo; the second mode (REOF2) represents the northern part, the third mode (REOF3) represents the western part and the fourth (REOF4) represents the north-eastern part of Togo.The Mann-Kendall test has revealed an increasing and significant trend of rainfall in the northern region of Togo.In contrast, the trends were not significant in the southern and north-eastern parts of the country.These results form a basis on which adaptation strategies may be taken in this region with high rainfall variability.

Key concepts: Geography, Wet season, Climatology, Precipitation, Physical geography, Environmental science, Geology, Cartography

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