Photo courtesy of
			Arne Bomblies

Broadly, my research is centered on the impacts of climate change and variability. Within this context, there are two specific focus areas of my research: disease/climate connections and regional adaptation to climate change. I introduce two ongoing projects below.

Understanding the impacts of extreme precipitation on watershed hydrology and infrastructure

The pursuit of climate change-resilient infrastructure involves the need to adapt to the rising incidence of extreme floods associated with nonstationarity (temporally changing statistics) in river flow. Throughout the United States, river flows are currently exhibiting changing flow statistics and in some cases higher frequency of extremes. This trend is noted in the northeast. Transportation infrastructure is designed under the assumption of stationarity (unchanging statistics), and therefore bridges and culverts are vulnerable and may not provide service for the duration of their design life. Streamflow nonstationarity in the northeastern United States is thought to arise from changes in the precipitation climatology associated with global climate change, changing land use in the watershed, and the effects of internal variables such as soil moisture variability and snowpack extent and persistence. All of these variables are sensitive to climate change to various degrees. Our research seeks to quantify the fundamental physical mechanisms underlying the observed nonstationarity using distributed hydrology models coupled to land use change models. We are studying the Winooski watershed of Vermont, which was recently heavily impacted by a series of extreme floods and is representative of much of the northeast, as a test case for detailed study.

A schematic showing recent research foci related to flood risk to transportation infrastructure.

Flood waters in the Winooski River at Winooski, Vermont following Tropical Storm Irene. August 29, 2011.

Climate change impacts on malaria transmission

We are studying the complex, nonlinear connections between disease transmission and the environment, specifically focused on malaria.We explore the spatial- and temporal variability of temperature, rainfall, and land cover as determinants of malaria risk in Africa. The disease/environment systems are then represented in mechanistic computer models. The model employs an agent-based structure to simulate mosquito populations that are observed in field settings (Asendabo, Ethiopia and Banizoumbou and Zindarou, Niger). This model technique has the advantage of maintaining the spatial structure of the population and allows complexity in disease dynamics to be investigated. Significant contributions have arisen from this approach, for example a recent result is an analysis of impacts resulting from changes in intraseasonal rainfall patterns (publication #10). That study shows that much observed variability in mosquito abundance can be explained by the temporal pattern of rainfall, in addition to the seasonal amount. We have also studied the impacts of changing land use on malaria transmission in Ethiopia (publication #11). We have set up a field site in the highlands of Ethiopia to gather field data for studying the complexity underlying malaria/environment/climate interactions. This is a test site which has been monitored for mosquito populations, hydrology conditions, land use changes, and climate since 2009.

Typical larval habitat for Anopheles gambiae in Asendabo, Ethiopia

Hydrology model output (snapshot of hourly output) showing spatial extent of simulated mosquito breeding habitat superimposed on an aerial photo of Banizoumbou, Niger.