UC Irvine

A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with an increase in malaria risk but the underlying mechanism is not well understood. While useful in investigating transmission dynamics, malaria models are limited in irrigated areas as they typically infer mosquito abundance from rainfall. Fundamentally, this assumes rainfall as a proxy for larval habitats and can lead to contradictory transmission results. The availability of water for breeding is governed by hydrological processes which are highly non-linear and spatially variable. The incorporation of hydrologic modeling is therefore essential to understand the effect of irrigation on malaria.

The overarching goal of this dissertation is to study the impact of irrigation on the spatiotemporal distribution of malaria larval habitats and transmission using a hydrology-based malaria model. First, a three-dimensional, distributed hydrologic model was applied to simulate malaria larval habitats at a sugarcane plantation site in Arjo, Ethiopia. The scarcity of field data was overcome by integrating remotely sensed data with the model. Results suggest that at least half of the irrigated farms had a high probability of larval habitat occurrence. Irrigation dampened and prolonged the seasonality of the larval habitats, with a significant shift from semi-permanent to permanent habitats.

Land surface depressions highly influence the transformation of rainfall to ponding for aquatic larval habitat formation. Hence, I sought to improve the representation of physically meaningful surface depressions in the digital elevation model (DEM) used in the hydrologic model. A new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P), was developed and evaluated through a case study in a pond-dominated watershed in the United States. D2P successfully resolved 86% of the ponds at 10 m DEM resolution. A hydrologic simulation was performed using the D2P processed DEM and demonstrated a more robust characterization of surface water dynamics.

Lastly, I extended the Arjo hydrologic modeling framework with D2P to enhance the larval habitat estimation in an agent-based malaria model and examine transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitats which resulted in significantly lower transmission. The application of irrigation enabled the development of mosquitoes in dry seasons while stabilizing growth in rainy seasons. The model also revealed that malaria transmission was sustained all-year round and intensified during the main transmission season, with the peak shifted forward by around one month. Finally, I showed how habitat heterogeneity could affect the spatiotemporal dynamics of malaria transmission. Understanding changes to malaria transmission dynamics by irrigation is important for the development of mitigation strategies. The framework presented in this dissertation particularly helps larval source management as a supplementary vector control by identifying malaria hotspots and prioritizing resources for operational planning.