Satellite gravimetry observations as provided by the GRACE and GRACE-FO missions have revolutionized our understanding of the global water cycle under climate change. However, the available time span of slightly more than 20 years is still relatively short for isolating long-term climate related signals such as trends or changes in the frequency of extreme events, as the water storage time series might still be masked by dominating interannual (natural) variations. Therefore, several approaches have been introduced in recent years to extend the GRACE/-FO data record into the past by exploiting additional data sets and innovative methodology, e.g. based on machine learning approaches.
In this study, we present a reconstruction of terrestrial water storage observed by GRACE (TWS) using a modified spatio-temporal graph neural network tailored for multivariate time series (Wu et. al 2020). Input features include multiple climate and hydrological variables from the ERA5 reanalysis, such as precipitation, evapotranspiration and runoff. The model architecture combines graph convolution modules to capture spatial dependencies and temporal convolution modules to learn
temporal patterns. A designed adjacency matrix encodes relationships between regions based on both geographic distance and similarity in the historical time series. In contrast to traditional deep learning approaches that rely on large input matrices, our method exploits the inherent efficiency of graph-based data structures by explicitly encoding time series data for each feature within individual nodes.

Lara Johannsen, Lukas Arzoumanidis, Youness Dehbi, Annette Eicker
HafenCity University Hamburg, Germany