Modeling and forecasting land surface wetness conditions, mosquito abundance, and mosquito-borne disease transmission

In this dissertation, I examine how variability of land surface wetness can affect mosquito abundance and mosquito-borne disease transmission. Using a TOPMODEL-based hydrology (TBH) model, my collaborators and I identify empirical relationships between modeled local land surface wetness and the abundance of several mosquito species in a New Jersey watershed. We then modify the TBH model to account for a more complete range of hydrologic processes, including discharge flow from perched water tables and from pockets of near surface saturated regions. We next apply this modified TBH model to south-central Florida and identify the mechanism by which drought amplifies the prevalence of St. Louis Encephalitis (SLE) virus within the local mosquito population. We also show the means by which this higher SLE virus prevalence is maintained throughout an epidemic, leading to SLE virus transmission to humans. My collaborators and I then develop skillful seasonal ensemble hydrologic forecasts using the TBH model and seasonal climate predictions. Using empirical relationships between modeled land surface wetness and SLE virus transmission, we then develop skillful seasonal forecasts of SLE virus transmission in south-central Florida and present a real-time forecast.; The results in Florida show that during the critical period of amplification the vector mosquito populations are quite localized, and thus susceptible to targeted control efforts. Identification of these amplification ‘hotspots’ using modeled hydrology will permit control of the Florida SLE virus vector before infection rates increase and transmission to humans occurs.