Global long-lead forecast of total water storage and evaluation for 2010-2024

Since 2002, the GRACE and GRACE Follow-On (GRACE/-FO) missions monitored total water storage changes (TWSC) – the amount of water stored on continents – with an unprecedented accuracy. However, there is a latency of a few months in generating the standard GRACE TWSC product while operational services that aim at such as forecasting drought or flood potentials require near-real time or even forecasted total water storage maps. In this study, we use machine learning to forecast the global GRACE-like total water storage change at long-lead times (up to one year ahead), where our approach works with lagged/forward-moving hydrometeorological variables/indices (e.g., precipitation) as predictors. We evaluate these data-driven TWSC forecasted from a hindcast experiment over 2010-2024 using GRACE observations and compare them to the existing model forecast products from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s new long-range forecasting system (SEAS5). We find that the presented approach performs overall better than the land surface model (HTESSEL) as used by the ECMWF SEAS5 in forecasting TWSC. We forecast global TWSC at a grid resolution of 1° using three GRACE mascon solutions, resulting in three forecasted GRACE-like TWSC datasets. Possible applications include large-scale drought and flood early warning, constraining and downscaling water storage in hydrological forecasting models, the sea level forecasting, interpreting total water storage changes during the latency period of GRACE/-FO data, and geodetic applications like forecasting Earth orientation parameters via hydrological angular momentum excitation or forecasting loading corrections in GNSS and altimetry data analysis.