Spatiotemporal Deep Learning for Snow-Water Equivalent Prediction

Automated Snow Telemetry (SNOTEL) networks provide critical hydrologic data with broad global socioeconomic, political, and environmental impacts. In the Western United States and European Alps, Snow Water Equivalent (SWE) is the backbone of the agricultural industry in addition to being a key source of municipal drinking water, making SWE forecasting critical for water policy and management as climate change alters year-to-year accumulation. While process-based snow models have long been used to predict SWE, machine learning approaches have risen to prominence in recent years due to their strong performance relative to observations.

SNOTEL measurements exhibit strong spatial (among neighboring sites) and temporal (day to day) correlations. However, despite the use of modern high-parameter approaches to explain spatial relationships, deep learning methods for SWE prediction fail to account for patterns among proximate locations, thus yielding inaccurate SWE predictions. We propose a novel approach to this problem by first using a Gaussian whitening process to remove spatial correlation from SWE measurements, static station features, and meteorological forcings before leveraging deep learning for temporal prediction; specifically, we train a Long Short-Term Memory (LSTM) model to learn SWE seasonality. This allows the LSTM to learn a clean temporal signal at each location without needing to implicitly approximate the underlying spatial covariance structure. After prediction, we re-introduce spatial dependence through the inverse of the whitening transformation, yielding spatially sound SWE estimates consistent with the original covariance.

The separation of spatial and temporal components makes this model more accurate than previous LSTM and high-parameter methods: we show our low-parameter process attains 22% better predictive success than the daily climatology baseline using Root Mean Squared Error (RMSE) and exceeds predictive accuracy of modern attention-based models with more than 92% of SNOTEL stations achieving Nash-Sutcliffe Efficiency (NSE) values greater than 0.5 while surpassing mean/median NSE of previous field-leading LSTM approaches. The success of this approach for point estimation provides a novel method for SWE accumulation forecasting on subseasonal scales or projecting SWE with future climate change data while motivating and supporting future work in predicting a large-scale, spatiotemporally complete SWE map.