Paul Isolo Mukwaya

Enhancing Security and Resilience of Low-Income Communities to Climate Change in Growing Cities: An Assessment of Flood Management and Planning Regimes in Kampala City, Uganda

The rapid expansion of settlements in cities and worsening economic inequality have shifted the balance of disaster risk from rural to urban areas. People have to survive in a money economy and have to contend every day with many socio-economic and environmental hazards. Studies of flood hazard risks in Kampala are inconclusive. Lack of reliable information makes it difficult to evaluate the different impacts of integrated flood management and planning regimes on the livelihoods of low-income households. An ongoing study in the city of Kampala is spatially determining the magnitude and intensity of exposure to flood hazards and their impact on the livelihoods of communities occupying low-income areas.

An examination of historical and future land use changes in Uganda using change detection methods and agent-based modelling

We analyse historical land use/land cover changes beginning from 1996 to 2013 and apply an agent-based model to simulate potential agricultural land use change scenarios in Uganda. The model incorporates farmers’ decision-making processes into biophysical and socioeconomic factors and uses these to analyse the effect of farmers’ decisions on agricultural land use changes. Geographic information system tools are employed to build spatial relations between farmers and land cover systems. Satellite images are used to represent the initial land cover conditions and serve as observed land cover datasets to calibrate the simulated results. Significant agricultural and grassland cover and urban land uses are experienced in 72 and 36% of the regions, respectively, while wetland land uses increased significantly in 82% of the regions. On the other hand, 91% of the regions experience reductions in forest cover except for Teso region which reports gains of 62% in forest cover. Acholi is the only region (extreme outlier) that reports dramatic gains in wetlands of over 880%. The results of the simulation model are promising, and the model was successful at representing historical and future scenarios of agricultural land use patterns at a national level.

Prediction of future malaria hotspots under climate change in sub-Saharan Africa

Malaria is a climate sensitive disease that is causing rampant deaths in sub-Saharan Africa (SSA) and its impact is expected to worsen under climate change. Thus, pre-emptive policies for future malaria control require projections based on integrated models that can accommodate complex interactions of both climatic and non-climatic factors that define malaria landscape. In this paper, we combined Geographical Information System (GIS) and Bayesian belief networks (BBN) to generate GIS-BBN models that predicted malaria hotspots in 2030, 2050 and 2100 under representative concentration pathways (RCPs) 4.5 and 8.5. We used malaria data of children of SSA, gridded environmental and social-economic data together with projected climate data from the 21 Coupled Model Inter-comparison Project Phase 5 models to compile the GIS-BBN models. Our model on which projections were made has an accuracy of 80.65% to predict the high, medium, low and no malaria prevalence categories correctly. The non-spatial BBN model projection shows a moderate variation in malaria reduction for the high prevalence category among RCPs. Under the low prevalence category, an increase in malaria is seen but with little variation ranging between 4.6 and 5.6 percentage points. Spatially, under RCP 4.5, most parts of SSA will have medium malaria prevalence in 2030, while under RCP 8.5, most parts will have no malaria except in the highlands. Our BBN-GIS models show an overall shift of malaria hotspots from West Africa to the eastern and southern parts of Africa especially under RCP 8.5. RCP 8.5 will not expand the high and medium malaria prevalence categories in all the projection years. The generated probabilistic maps highlight future malaria hotspots under climate change on which pre-emptive policies can be based.