Hierarchical Graph Networks for ForecastingTerrestrial Water Storage Anomalies

The availability of fresh water is vital to the ecosystem and communities. In a changing climate, the increased risk of droughts makes it crucial to have an accurate understanding of changes in terrestrial water storage (TWS). Predicting changes in TWS is inherently difficult since it integrates the changes of all water compartments, with underlying processes that operate on vastly different temporal and spatial scales. 

Forecasting tasks nowadays are often solved using machine learning models. However, these models require vast amounts of data. In contrast, total water storage anomalies (TWSA) derived from GRACE/GRACE-FO observations only date back to 2002 and are available at a grid of 1°x1° at monthly resolution. Nevertheless, Li et al., (2024) showed that machine-learning approaches could forecast TWSA tendencies for up to one year ahead. They cleverly exploit temporal lag relationships between TWSA and ocean, atmospheric, or land variables.

In our work, we explore a novel design of a hierarchical graph using domain knowledge of hydrological basins to encode these processes in a latent feature sequence using an encoder-processor-decoder style graph neural network. The subsequent recurrent neural network then forecasts TWSA from the latent feature sequence and 12-month history of TWSA for up to six months ahead. The gridded product of the seasonal forecast of global TWSA shows improvement over a seasonal long-term mean.

Li, F., Kusche, J., Sneeuw, N., Siebert, S., Gerdener, H., Wang, Z., Chao, N., Chen, G., and Tian, K.: Forecasting Next Year’s Global Land Water Storage Using GRACE Data, Geophys. Res. Lett., 51, e2024GL109101, https://doi.org/10.1029/2024GL109101, 2024.