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Larval source management (LSM) has so far not been widely used in malaria vector control in Africa, partly due to the lack of understanding of the hydrological mechanism of aquatic habitat formulation and stability. LSM would be greatly facilitated if larval habitat distribution can be predicted a priori so that areas best suitable or unsuitable to LSM can be identified. To this end, we propose combining an integrated high resolution distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of climate and irrigation. The model was calibrated using …