This proposal relates to two of the eight application areas: water quality and public health. In the Northeastern United States, climate change scenarios typically indicate an increase in overall precipitation, which would lead to larger river discharge. Increased discharge would then lead to greater pathogen loading in rivers and coastal waters from anthropogenic sources due to runoff from contaminated sites. This project is designed to aid in two different, but related, decision making activities related to these phenomena: 1) the prediction of beach closings due to poor water quality and 2) the prediction of future wastewater treatment needs. The central objective of this study is to determine if a regression model can be used to predict water quality along the coast of Maine. The main hypotheses for this research are: 1) accurate measurements of precipitation, land-use, and river discharge can be used to develop regression model which can achieve accurate predictions of water quality and 2) this model can be expanded to larger temporal and spatial scales. In order to test these hypotheses, we will utilize NASA imagery from Landsat to quantify land use/cover change in the Northeast. River data from USGS and other sources will allow us to quantify the hydrology and nutrient discharges. Water quality data obtained from state agencies will allow us to calibrate our model and make predictions of water quality for these same state agencies. Information from NASA atmospheric models will be used to determine the feasibility of this model?s use in climate scenarios. The data gathered here would also be useful to future NASA missions such as GPM, SMAP and LDCM, by demonstrating their utility. Once validated, the model can be easily expanded to national or global spatial scales.