Land TWSA forecasting

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor mission GRACE Follow-On (GRACE-FO) have provided critical insights into terrestrial water dynamics by measuring total water storage anomolies (TWSA) across the globe. These satellite-derived observations offer an unprecedented view of how water is stored on continents, informing a wide range of scientific and practical applications, including climate monitoring, hydrological modeling, and drought assessment. However, the utility of standard GRACE/-FO TWSA products is constrained by a latency of several months, limiting their effectiveness for real-time applications.

To address this gap, we present a semi-operational dataset—updated regularly or upon request—called the Global Land Water Storage Forecast Release 1 (GLWFC1.0). This dataset is generated using a machine learning approach that trains on solely observational and reanalysis-based hydrometeorological predictors against the JPL GRACE/GRACE-FO mascon solution. The forecasts extend up to one year ahead of each month from January 2024 onward and are provided at a 1° grid resolution.

This dataset can support a broad range of applications, including near-real-time drought early warning, constraining and downscaling hydrological forecasting models, sea level forecasting, and geodetic studies such as forecasting Earth orientation parameters (EOP) via hydrological angular momentum. By providing timely, observation-driven forecasts, this product offers a practical alternative for bridging the delay in standard GRACE/-FO data releases. We invite you for testing and evaluating this data set and we would appreciate feedback. When used in presentations or publications, please cite Li et al. (2024, 2025).

References:

• Li, F., Springer, A., Kusche, J., Gutknecht, B., Ewerdwalbesloh, Y. (2025). Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation. Water Resources Research, e2024WR037926.
• Li, F., Kusche, J., Sneeuw, N., Siebert, S., Gerdener, H., Wang, Z., ... & Tian, ​​K. (2024). Forecasting next year's global land water storage using GRACE data. Geophysical Research Letters, 51(17), e2024GL109101. 

Contact:

If you have any questions regarding this data set, please contact Fupeng Li