Modeling Mountain Snowpack Dynamics with CGANS: A Validation Study

In our previous work [1] we show the effectiveness of learning based schemes to emulate numerical simulations. Specifically, we explore the use of GANs to model Snow Water Equivalent (SWE). In this work, we present further analysis of these results, with a model now trained on 30 years of SWE data compared to 10 years for the previous. First, we study the model outputs with station data and find the outputs to closely match the station data. Second, we study the uncertainty of the cGAN based results. We study the correlation between noise and the outputs generated. We hope these contributions can help the community leverage the power of these deep learning models for future works.