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. 2002 Jan;8(1):6-13.
doi: 10.3201/eid0801.010049.

Using a dynamic hydrology model to predict mosquito abundances in flood and swamp water

Jeffrey Shaman  1 , Marc Stieglitz, Colin Stark, Sylvie Le Blancq, Mark Cane
Affiliations

Using a dynamic hydrology model to predict mosquito abundances in flood and swamp water

Jeffrey Shaman et al. Emerg Infect Dis. 2002 Jan.

Abstract

We modeled surface wetness at high resolution, using a dynamic hydrology model, to predict flood and swamp water mosquito abundances. Historical meteorologic data, as well as topographic, soil, and vegetation data, were used to model surface wetness and identify potential fresh and swamp water breeding habitats in two northern New Jersey watersheds. Surface wetness was positively associated with the subsequent abundance of the dominant floodwater mosquito species, Aedes vexans, and the swamp water species, Anopheles walkeri. The subsequent abundance of Culex pipiens, a species that breeds in polluted, eutrophic waters, was negatively correlated with local modeled surface wetness. These associations permit real-time monitoring and forecasting of these floodwater and nonfloodwater species at high spatial and temporal resolution. These predictions will enable public health agencies to institute control measures before the mosquitoes emerge as adults, when their role as transmitters of disease comes into play.

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Figures

Figure 1
Figure 1
Visualization of the processing of land surface topography for a sample 25-km2 area in New York State. a) Digital Elevation Model--a pixelated (10 m cells) representation of land surface topography. Contour lines (in meters) have been overlain. b) Postprocessing, depiction of land surface wetness at a single point in time. Blue areas are wettest. The variability and spatial distribution of surface wetness are evident.
Figure 2
Figure 2
Schematic depiction of the hydrology model. The model couples the analytic form of TOPMODEL equations within a single column framework. From an update of the mean water table depth, TOPMODEL equations and Digital Elevation Model data are used to generate baseflow and the saturated fraction of the watershed.
Figure 3
Figure 3
Aedes vexans collections at the Great Swamp, 1987 - 1994.a aConsiderable year-to-year variability is evident in these light trap collections. Note the different scaling for 1988 and 1989.
Figure 4
Figure 4
Time-series regression model fit of Aedes vexans 10 days later at the Bernaski site, Pequest River catchment. Regression fit is significant at p<0.01, r-squared = .50.
Figure 5
Figure 5
Logistic regression analysis of the complete 15-year record of Great Swamp site Aedes vexans. Left vertical axis provides the predicted probability that the count of mosquitoes will lie at or below a given threshold. Surface wetness (the index of local wetness [ILM]) increases from left to right. Dots between lines illustrate the distribution of ILW values for mosquito counts falling between two successive threshold line values.
Figure 6
Figure 6
a) Mass emergence forecast, Aedes vexans. Mass emergence is defined as a single-day collection of ≥128 mosquitoes. The probability of a mosquito mass emergence (lagged 10 days) increases with modeled surface wetness. b) Mass emergence forecast, Anopheles walkeri. Based on logistic regression analysis of the 15-year record of Great Swamp site An. walkeri. Mass emergence is defined as a single-day collection of ≥32 An. walkeri. As per Figure 6a, the probability of a mosquito mass emergence (lagged 10 days) increases with increasing modeled surface wetness. c) Mass emergence forecast, Culex pipiens. Based on logistic regression analysis of the 15-year record of Great Swamp site Cx. pipiens. Mass emergence is defined as a single-day colleciton of ≥32 Cx. pipiens. The probability of a mosquito mass emergence (lagged 10 days) decreases with increasing modeled surface wetness.
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