In recent years, the rapid development of cloud computing and web technologies has led to a significant advancement to chain geospatial information services (GI services) in order to solve complex geospatial problems. However, the construction of a problem-solving workflow requires considerable expertise for end-users. Currently, few studies design a knowledge base to capture and share geospatial problem-solving knowledge. This paper abstracts a geospatial problem as a task that can be further decomposed into multiple subtasks. The task distinguishes three distinct granularities: Geooperator, Atomic Task, and Composite Task. A task model is presented to define the outline of problem solution at a conceptual level that closely reflects the processes for problem-solving. A task-oriented knowledge base that leverages an ontology-based approach is built to capture and share task knowledge. This knowledge base provides the potential for reusing task knowledge when faced with a similar problem. Conclusively, the details of implementation are described through using a meteorological early-warning analysis as an example.

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