On the use of satellite-based estimates of rainfall temporal distribution to simulate the potential for malaria transmission in rural Africa

[1] This paper describes the use of satellite-based estimates of rainfall to force the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a hydrology-based mechanistic model of malaria transmission. We first examined the temporal resolution of rainfall input required by HYDREMATS....

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Bibliographic Details
Main Authors: Yamana, Teresa K. (Contributor), Eltahir, Elfatih A. B. (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor), Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology) (Contributor)
Format: Article
Language:English
Published: American Geophysical Union (AGU), 2013-03-12T19:32:57Z.
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Summary:[1] This paper describes the use of satellite-based estimates of rainfall to force the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a hydrology-based mechanistic model of malaria transmission. We first examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 h resolution. We then investigated whether HYDREMATS could be effectively forced by satellite-based estimates of rainfall instead of ground-based observations. The Climate Prediction Center morphing technique (CMORPH) precipitation estimates distributed by the National Oceanic and Atmospheric Administration are available at a 30 min temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results demonstrate that adjusted rainfall estimates from satellites can be used with a mechanistic model to accurately simulate the dynamics of mosquito populations.
National Science Foundation (U.S.) (Grant EAR ‐ 0946280)
National Science Foundation (U.S.) (Grant EAR ‐ 0824398)