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University of Cape Town (2014)

Simulating the characteristics of droughts in Southern Africa

Ujeneza, Eva Liliane

Titre : Simulating the characteristics of droughts in Southern Africa

Auteur : Ujeneza, Eva Liliane

Université de soutenance : University of Cape Town.

Grade : Master’s of Science in Environmental and Geographical Science 2014

Résumé
Drought is widely considered as one of the most devastating natural disasters in the world. In particular, drought is a big threat in Southern Africa because the economy of most of the population in the region is based on rain-fed agriculture. Previous studies have projected that global warming may enhance the frequency and intensity of droughts over Southern Africa in the future. However, the credibility of this projection depends on the ability of the global and regional climate models (GCMs and RCMs) in simulating the characteristics of drought. This thesis presents the characteristics of the Southern African droughts and evaluates the capability of global and regional climate models in simulating these characteristics. The thesis used a multi-scaled standardized drought index (called standardized precipitation evapo-transpiration index, SPEI) in characterizing droughts at 3- and 12-month scales over Southern Africa. The spatial patterns of the droughts are identified using the principal component analysis (PCA) on the SPEI, while the temporal characteristics of the drought patterns are studied using wavelet analysis. The relationship between each drought pattern and global SSTs (and climate indices) is quantified using correlation analysis and wavelet coherence analysis. The study uses correlation analysis to quantify the capability of the models in simulating the drought patterns. The PCA results show four main drought patterns that jointly explain about 50% of SPEI variance over Southern Africa. The drought patterns (hereafter PF1, PF2, PF3 and PF4) have their maximum loadings over the south-western part of Southern Africa (i.e. the common border of South Africa, Botswana and Namibia), Zimbabwe, Tanzania, and Angola, respectively. PF1, PF2 and PF4 are strongly correlated with sea-surface temperature (SST) over the South Atlantic, Tropical Pacific and Indian Oceans, while PF3 is strongly correlated with the SST over the Tropical Pacific, Atlantic and Indian Oceans. The drought patterns also have significant coherence with some climate indices, but the strength, duration, and phase of the coherence vary with time. The results show that GCMs and RCMs simulate the spatial patterns of drought better at 3 month scale than at 12-month scale. At a 3-month scale, 70% of the GCMs simulate all drought patterns with a high correlation coefficient (r > 0.6), but at a 12-month scale, none of the GCMs simulate all drought patterns with such a high correlation, although 60% of the GCMs reproduce at least three of the drought patterns with a high correlation coefficient (r > 0.6). Similarly, at 3-month scale, 75% of the RCMs simulate all drought patterns with a high correlation coefficient (r > 0.6), while at a 12-month scale, only 25% of the RCMs simulate all drought patterns with such a high correlation coefficient, but 90% of the RCMs reproduce at least three of the drought patterns with a high correlation coefficient (r > 0.6). The results of this study have applications in using the GCMs and RCMs for seasonal prediction of droughts, and for studying the impacts of global warming on droughts, in Southern African.

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Page publiée le 31 janvier 2019