This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Dynamical malaria models can relate precipitation to the availability of vector breeding sites using simple models of surface hydrology. Here, a revised scheme is developed for the VECTRI malaria model, which is evaluated alongside the default scheme using a two year simulation by HYDREMATS, a 10 metre resolution, village-scale model that explicitly simulates individual ponds. Despite the simplicity of the two VECTRI surface hydrology parametrization schemes, they can reproduce the sub-seasonal evolution of fractional water coverage. Calibration of the model parameters is required to simulate the mean pond fraction correctly. The default VECTRI model tended to overestimate water fraction in periods subject to light rainfall events and underestimate it during periods of intense rainfall. This systematic error was improved in the revised scheme by including the a parametrization for surface run-off, such that light rainfall below the initial abstraction threshold does not contribute to ponds. After calibration of the pond model, the VECTRI model was able to simulate vector densities that compared well to the detailed agent based model contained in HYDREMATS without further parameter adjustment. Substituting local rain-gauge data with satellite-retrieved precipitation gave a reasonable approximation, raising the prospects for regional malaria simulations even in data sparse regions. However, further improvements could be made if a method can be derived to calibrate the key hydrology parameters of the pond model in each grid cell location, possibly also incorporating slope and soil texture. Copyright:
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© 2016 Asare et al.
Asare EO, Tompkins AM, Bomblies A. A regional model for malaria vector developmental habitats evaluated using explicit, pond-resolving surface hydrology simulations. PLoS One. 2016 Mar 22;11(3):e0150626.