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.

Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socioeconomic infrastructures and human habitats. This research, basing on geospatial techniques, focuses on coastal erosion monitoring from 1988 to 2018 in Togo. It is interested in the extraction of shoreline and in the analysis of change. Various satellite images indexes have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline on NDWI index. The topographic morphology such as linear and non-linear coastal surfaces have been considered. The estimation of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations