Ashray Manepalli (terrafuse); Adrian Albert (terrafuse, inc.); Alan Rhoades (Lawrence Berkeley National Lab); Daniel Feldman (Lawrence Berkeley National Lab)
Process-based numerical simulations, including those for climate modeling applications, are compute and resource intensive, requiring extensive customization and hand-engineering for encoding governing equations and other domain knowledge. On the other hand, modern deep learning employs a significantly simpler and more efficient computational workflow, and has been shown impressive results across a myriad of applications in the computational sciences. In this work, we investigate the potential of deep generative learning models, specifically conditional Generative Adversarial Networks (cGANs), to simulate the output of a physics-based model of the spatial distribution of the water content of mountain snowpack - the snow water equivalent (SWE). We show preliminary results indicating that the cGAN model is able to learn diverse mappings between meteorological forcings and SWE output. Thus physics based cGANs provide a means for fast and accurate SWE modeling that can have significant impact in a variety of applications (e.g., hydropower forecasting, agriculture, and water supply management). In climate science, the Snowpack and SWE are seen as some of the best indicative variables for investigating climate change and its impact. The massive speedups, diverse sampling, and sensitivity/saliency modelling that cGANs can bring to SWE estimation will be extremely important to investigating variables linked to climate change as well as predicting and forecasting the potential effects of climate change to come.