Integrating Non-Climate Stressors (Land Use and Malaria Incidences) with Climate Change Indicators to Map Livelihood Vulnerability in Malawi and Mozambique.

The study uses Satellite Model Datasets, including Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Climate Research Unit (CRU), during the primary rainfall season from 1981-2020, Normalized Difference Vegetation Index, and Malaria incidences in mapping vulnerability of livelihoods. The specific objectives were to: (i) analyze the decadal precipitation trends during the primary season from 1981-2020 for Malawi and Mozambique, (ii) map and detect vegetation types and changes over time in Malawi, and (iii) predict the malaria incidence distribution under climate change in Malawi. Pronounced decadal variability (inconsistent decade-to-decade trends) was observed for the monthly precipitation mean in Malawi and Mozambique from 1981-2020. However, the seasonal mean precipitation shows a consistent increase in decade-to-decade drying for the northeastern region, especially in CHIRPS. CRU data shows patches of extreme dryness across the entire study domain, including the southern region, during recent decades (2011-2020), despite the increased frequency of floods associated with tropical cyclones from 2018 to the present. NDVI indicated that the northern Malawi has more healthy vegetation than the central and southern regions. Support Vector Machine and Unsupervised Classification also revealed more vegetation types in the northern region than the other regions. The southern region was also associated with increased malaria incidences due to high population, high illiteracy levels, poor housing conditions, and lower income levels among others. High cases of malaria incidences were also noted along the lake shore. Land characteristics also played a role in the high cases of malaria incidences. This study uses geospatial techniques to merge the combined impacts of the recent climate change indicators and these non-climate stressors to identify nuanced climate change livelihood hotspots. This is important for developing hotspots of compounded events necessary for effective local and regional scale adaptive and mitigation strategies.