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This preliminary descriptive study describes the temporal and spatial distribution of US waterborne disease outbreaks using a geographic information system approach in relationship to rainfall.Regional climate change and variability and their effect on water resources have not been the subject of much study. Climate predictions suggest that storms will be of greater intensity and that the average precipitation event is likely to be heavier. Rainfall and runoff have been associated with individual outbreaks of waterborne disease caused by fecal‐oral pathogens. Waterborne disease outbreak data from 1971 through 1994 were analyzed for groundwater and surface water in 2,105 US watersheds. Between 20 and 40 percent of outbreaks were associated with extreme precipitation. This relationship with extreme precipitation was found to be statistically significant for both surface water and groundwater, although it was more apparent with surface water outbreaks. The authors offer recommendations for improving the assessment of changes in water quality and the effect that climate variability and environmental factors have on waterborne disease risk.

[1] We used a process-based ecosystem model (Marine Biological Laboratory General Ecosystem Model (MBL-GEM III)) to predict and analyze biogeochemical responses of Arctic tundra ecosystems to past and future (2001-2100) changes in climate and atmospheric CO 2 in the Kuparuk River Basin, Alaska. We first calibrated the model by deriving a single parameter set that closely simulated the response of moist tussock tundra to decade-long experimental manipulations of nutrients, temperature, light, and atmospheric CO 2 at Toolik Lake on the North Slope of Alaska. We then applied the parameterized model to the entire Kuparuk River Basin over 180 years. The model predicted that warming and drying resulted in a short-term source of CO 2 on annual timescales but resulted in a CO 2 sink on decadal timescales. These predictions are consistent with recent measurements. A time series analysis has identified that while the immediate response to warming is to release C, the response a year later is to store C. This 1-year lag is consistent with other work that has shown a similar lag in C storage and normalized difference vegetation index (NDVI) on a global scale. Our simulation results indicated that by 2100 high CO 2 and warming will increase C sequestration, mostly as a result of (1) an increase in vegetation C:N ratio, which occurs across the Kuparuk Basin, and (2) a redistribution of N from soils (with low C:N ratios) to vegetation (with high C:N ratios), which occurs mainly in ecosystems in the basin that are initially productive, dry, and warm. These results are consistent with the observation of increased shrubiness in Alaskan tundra over the past few decades. Our application of the model has been hindered by the lack of climate data for the region, especially precipitation. A number of other general issues have been identified for making progress in modeling spatial and temporal C dynamics of Arctic tundra.

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