Creating a high-resolution Northern Hemisphere daily SWE dataset (1980-2020) using Machine Learning

Snow water equivalent (SWE) is an important component of the global hydrological cycle, acting as a primary reservoir for seasonal water storage. Despite its relevance, only few datasets are available that provide long-term daily SWE estimates at global scale. Even amongst the best gridded SWE products, the spatial resolution does not go beyond 10km, a significant limitation considering the large spatial variability of snow. Furthermore, assimilation of snow observations in such products remains another key challenge. Machine learning (ML) models and their combination with process-based simulations, what is known as Hybrid Modelling, offer a promising alternative for producing detailed SWE predictions at large scales, given their high inference speed and adaptability to their training data. Hybrid ML models have already been used for SWE prediction over a small number of sites, improving both pure ML approaches and advanced process-based snow models such as Crocus, but their applicability for long-term spatiotemporal modelling of snow at larger scales remains to be tested.

In this project, we trained an LSTM model using in-situ snow data from roughly 10000 sites throughout the Northern Hemisphere with the aim of creating a 40-year gridded dataset of daily SWE at 1 km resolution. The model incorporates temperature, precipitation, and shortwave radiation as meteorological predictors, alongside a small set of topographic variables and land cover classification. Preliminary results show a good fit to stations excluded from the training set, with an RMSE of 44 mm, where unequal distribution of observation locations was accounted for by a weighting scheme. These findings suggest the suitability of this approach for extending coverage to ungauged regions across the Northern Hemisphere. The use of the ERA5-Land SWE product as a hybrid support promises further improvements in model performance.

Ultimately, this project aims to provide a finer-scale alternative to existing daily SWE products. By improving the spatial resolution to 1km and incorporating available snow measurements, it contributes to a more refined view of seasonal snow storage across the Northern Hemisphere.