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Mathematical models are critical to predicting the effects of human activities and natural processes on water quality. Water quality decision support systems (DSS) such as BASINS (and the HSPF model embedded in BASINS) are used extensively to simulate watershed processes, allowing for the partitioning of the total load among the simulated land uses and thus, more effective nutrient management efforts. Such partitioning facilitates the establishment of total maximum daily loads (TMDLs) - a task mandated by USEPA under the Clean Water Act. Remote sensing products are currently used to partition pollutant loads among different land cover types, but may also be useful for characterizing significant variability in non-point source pollution from forests. Previous research has shown that data from MODIS and Landsat can greatly increase the predictability of stream nutrient concentrations in watersheds having a significant forest component. We will: ( 1) incorporate remotely sensed measures of forest condition in BASINS/HSPF to improve the calibration of the water quality in the model to (2) generate improved estimates of annual non-point source loads of nitrogen (N) from forests to surface waters. We will use data from three regions with considerable water quality data available for model calibration and validation: Chesapeake Bay Watershed, Adirondacks, and Wisconsin. End-users include: EPA, the agency who oversees the Clean Water Act and maintains the BASINS modeling framework, and the Maryland Department of the Environment and the Chesapeake Bay Program, both of whom are interested in improving water quality predictions to assist regional, state, and local agencies in performing water quality studies and establishing TMDLs in the region. The primary activities of the project are: (1) derivation of annual image-derived metrics of landscape condition; (2) incorporation of the derived metrics into calibration of BASINS/HSPF; and (3) interacting with e nd-users to incorporate the improvements into the decision-making process.