Achieving reproducible computational models and workflows is an important challenge that calls for open and reusable code and data, well-documented workflows, and controlled environments that allow others to verify published findings. Several scientists have highlighted the reproducibility crisis in science, but tools to help achieve reproducibility are limited. This presentation will describe cyberinfrastructure developed as part of the Geotrust and HydroShare projects that is enabling reproducible hydrologic science in the CUAHSI JupyterHub platform linked to HydroShare. Snow modeling plays an important role in the prediction of seasonal runoff and water supply forecasting for water resources management in snow-fed river basins. Physically based modeling is generally assumed better suited to reproduce snow processes under changing conditions. However, challenges exist in the application of snowmelt models that make them hard to reproduce, in terms of (1) tracking the preparation of model inputs and preserving the precise version of input data used, (2) repeating the execution of model code to duplicate model outputs, and (3) reproducing analyses used to report the results. This presentation will describe the use of Sciunit, which is software for creating self-contained and annotated containers that describe and package computational experiments, as deployed in the CUAHSI JupyterHub platform address these challenges. We will describe a research application of the Utah Energy Balance (UEB) snowmelt model to investigate water supply forecasts improvement for test watersheds in the Colorado River Basin that is made reproducible through the use of Sciunit. This work illustrates how reproducibility of the complete hydrologic science modeling cycle can be enhanced. The contents can be shared with other users in HydroShare to repeat or build on the work and can be permanently published to receive a digital object identifier for citation in papers to fulfill the open data mandate.