This is a USAID climate vulnerability mapping study of Mali, including climate scenario projections for future regional, and overall, climate vulnerability.
As part of the African and Latin American Resilience to Climate Change (ARCC) Project, Mali is the focus of this report presenting the results of a climate vulnerability mapping exercise intended to assist with strategic planning. The study utilised a spatial vulnerability index comprising of 18 indicators grouped into three vulnerability components: climate exposure, sensitivity, and adaptive capacity. This data is presented here in a variety of map outputs, including an overall vulnerability map.
Given the high levels of poverty, all of Mali’s territory and population could be said to be highly vulnerable to climate change. That said, Mali has a long history of coping with climate variability, and includes a diverse system of livelihoods. The purpose of this study is to identify areas of high relative vulnerability within Mali; hot-spots where high climatic-stress, high sensitivity, and low adaptive capacity combine.
The results show that large areas of northern Mali can be considered as being particularly vulnerable, although this sparsely populated area only accounts for around 6 per cent of Mali’s population. Meanwhile, 75 per cent of the population live in either the relatively low vulnerability areas of Bamako and the region immediately around Sikasso (due to high capacity and low sensitivity), or in the medium-high vulnerability of the most densely populated southeastern agricultural region.
With regard to changes in vulnerability over time, the study considered two statistically down-scaled climate scenarios (low and high emission) centred on both 2030 and 2050. While only modest changes are expected by 2030, by 2050 much of northern Mali will likely move from medium-high to high vulnerability. The northern limit for rain-fed millet and sorghum is expected to continue to shift downward as temperatures increase and moisture availability decreases.
Finally, limits to the analysis are discussed. The analysis could not adequately capture changes in rainfall variability, which are likely to have as much of an impact on livelihoods as long-term trends. Also, individual communities within each region will be more or less vulnerable than the average for a number of context-specific reasons. Further, the results rely on the robustness of the underlying data. To aid in the understanding of the margins of error involved, the study presents in the annex the aggregation approach used, the sensitivity analysis of the study itself, and the data limitations of the indicator metadata, together with all of the map outputs.