With the advent of the data grid came a novel distributed scientific computing paradigm known as service-oriented science. Among the plethora of systems included under this framework are scientific workflow management systems, which enable large-scale process scheduling and execution. To ensure quality of service, these systems typically seek to minimize workflow execution time as well as costs for slices of data grid access. The geospatial domain, among other sciences, involves yet another optimization factor, the accuracy of results. The relationship between execution time and workflow accuracy can often be exploited to offer more flexibility in handling user preferences. We present a system which meets user constraints through a dynamic adjustment of the accuracy of workflow results.