A combination of accelerated population growth and severe droughts have created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with increased malaria risk, but it remains difficult to understand the underlying mechanism and develop countermeasures to mitigate its impact. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent-based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two-fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi-permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all-year round and intensified during the main transmission season, with the peak shifted forward by around one month. Lastly, we demonstrated how habitat heterogeneity could affect the spatiotemporal dynamics of malaria transmission. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.

The Nile river basin is one of the global hotspots vulnerable to climate change impacts due to fast growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from the 6 Coupled Model Intercomparison Project (CMIP6) benchmarked against a high spatial resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR). Next, a Bayesian Model Averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is under-dispersive and a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change varies considerably across the Nile basin. Specifically, it is found that projected change in the two headwaters basins, namely Blue Nile and upper White Nile is 0.03% and -1.65% respectively; both statistically insignificant at 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.