Predicting Snow Water Equivalent in the Tuolumne River Basin, CA Through Time Series Forecasting Using Deep-Learning

Mountain snow fields act as natural storage basins in areas that rely on snowmelt, and by understanding the long-term trends of these systems, alongside the spatial component, we can make more informed decisions on their management and conservation. However, current flight intervals make the production of continuous airborne data limited. Our aim is to demonstrate the necessity of more temporal analysis and modeling of Snow water equivalent (SWE) for the management and conservation of water resources from the Tuolumne River Basin, and globally, through Time Series Forecasting and Uni/Multivariate analysis. In association with NASA SnowEx, we aim to create two Long-Short-Term Memory (LSTM) regression models to predict SWE, by itself and how it changes based on Snow Depth and Snow Density. The relevant raster data sets were collected by the ASO (Airborne Snow Observatory), and obtained from the NSIDC (National Snow and Ice Data Center).