We present the initial progress on the HydroFrame community platform using an automated Kepler workflow that performs end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization. We will demonstrate how different modules of workflow can be reused and repurposed for the three target user groups. Moreover, the Kepler workflow ensures complete reproducibility through a built-in provenance framework that collects workflow specific parameters, software versions and hardware system configuration. The poster will also present a design to leverage provenance data and machine learning techniques to predict performance and forecast failures ahead of time using an automatic performance collection component of the pipeline, with a goal to optimize utilization of large-scale computational resources to adjust to the needs of all three user groups.