Data provenance accepts and approves the scientists to model as to investigate the beginning of an unexpected value. It can be used as a duplicate recipe for output data products. The capturing provenance requires enormous effort by scientists in terms of time, training and need to design the workflow of the scientific model i.e., workflow source, which requires both time and training. Scientists may not document any workflow source before the model execution due to lack of time and training. And it is needed to capture provenance while the model is running, i.e., fine-grained data provenance.