Abstract
<jats:p>Abstract. This work presents an unsupervised deep-learning approach for the spatial downscaling of Terrestrial Water Storage Anomalies (TWSA) from their native coarse resolution to a 1° grid, using simulated Next-Generation Gravity Mission (NGGM) and ESA–NASA Mass-change And Geosciences International Constellation (MAGIC) products obtained from realistic end-to-end (E2E) closed-loop experiments carried out in the context of ESA studies, together with real Gravity Recovery and Climate Experiment (GRACE) and Follow-On (GRACE-FO) observations. A U-Net Convolutional Neural Network integrates hydro-climatic variables from ERA5-Land and topographic information to retrieve fine-scale TWSA patterns while keeping consistency with the original coarse gravimetric observations. Results over five regions of interest – Africa, the Amazon basin, Europe, the Ganges basin, and the Mississippi basin – have been evaluated through a composite score combining pixel-wise and structure-wise metrics. The analysis shows that the quality and native resolution of the input gravimetric products strongly influence the downscaled fields. In the simulated experiments, NGGM and MAGIC generally achieve higher high-resolution composite scores than the GRACE-C-like configuration, while monthly products outperform the corresponding 5-daily solutions. At the native satellite resolution, the aggregated downscaled fields remain highly consistent with the input products, with composite scores generally above 0.90 in the simulated and real scenarios. Water balance equation (WBE) analyses over the Congo and Danube basins show that the downscaled products generally preserve the basin-scale closure behavior of their corresponding satellite inputs, particularly for the 5-daily simulations, where the errors of the aggregated and 1° downscaled products remain close to the satellite baseline. The improved quality of the simulated observing scenarios is also reflected in lower WBE errors: from the GRACE-C-like to the MAGIC configuration, the 1° downscaled RMSE decreases from 4.82 to 3.32 mm over Congo and from 14.47 to 7.10 mm over Danube. For real GRACE(-FO) observations, the aggregated downscaled products reproduce the satellite-scale WBE residuals with NRMSE values of 0.014 over Congo and 0.007 over Danube. Although the fine-scale redistribution remains underconstrained in the absence of independent high-resolution observations, these findings demonstrate that unsupervised deep-learning downscaling can improve the spatial detail of satellite-derived TWSA while preserving the large-scale gravimetric and hydrological consistency of both simulated future-mission products and real GRACE(-FO) observations.</jats:p>