A myriad of recent activities can be seen towards dynamic workflow composition for processing complex and data intensive problems. Meanwhile, the simultaneous emergence of the grid has marked a compelling movement towards making datasets and services available for ubiquitous access. This development provides new challenges for workflow systems, including heterogeneous data repositories and high processing and access times. But beside these problems lie opportunities for exploration: The grid's magnitude offers many paths towards deriving essentially the same information albeit varying execution times and errors. We discuss a framework for incorporating QoS in a dynamic workflow composition system in a geospatial context. Specific contributions include a novel workflow composition algorithm which employs QoS-aware apriori pruning and an accuracy adjustment scheme to flexibly adapt workflows to given time restrictions. A performance evaluation of our system suggests that our pruning mechanism provides significant efficiency towards workflow composition and that our accuracy adjustment scheme adapts gracefully to time and network limitations.