Towards hydrological extreme event monitoring using deep learning-downscaled NGGM and MAGIC data

Terrestrial Water Storage Anomalies (TWSA) derived from satellite gravimetry provide a unique, integrated measure of large-scale hydrological conditions and have been widely used to characterize flood-prone states at basin scale. However, their applicability for flood monitoring and early warning is constrained by the coarse spatial resolution of current gravimetric products (approximately 300 km), which limits the detection of localized storage anomalies relevant for flood initiation and timing.

An unsupervised deep-learning approach based on convolutional neural networks is used to downscale TWS grids from Next-Generation Gravity Mission (NGGM) and ESA–NASA MAGIC constellation simulated products from 3° to 1° with a five daily frequency. With this new constellation, the NASA-DLR GRACE-C pair will ensure continuity and spatial coverage, while the ESA NGGM will provide increased sensitivity, novel products and pre-operational capabilities, contributing for over 90% to the MAGIC mission performance over the hydrologically relevant areas. The joint ESA-NASA MAGIC constellation will provide 5-daily gravity field products on a global scale with a spatial resolution of approximately 200 km, also reducing latency and uncertainties with respect to present gravimetry missions.

Simulated satellite products corresponding to the GRACE-C, NGGM and MAGIC mission scenarios are obtained from realistic end-to-end (E2E) closed-loop experiments carried out in the context of ESA studies, while the downscaling task is assigned to a U-Net module integrating ERA5-Land climate variables and the ETOPO2022 Digital Elevation Model. The ground truth solution of the gravity field is given by the ESA ESM 2.0, allowing for a-posteriori validation of the downscaling framework. The downscaled maps present high spatial and temporal correlation with the ground truth, reconstructing fine-scale TWS patterns without losing structural coherence at the native resolution of the satellite product. The pipeline is also extended to real GRACE/GRACE-FO data for real-world applications. The obtained results highlight the potential of unsupervised machine learning approaches for regional hydrological monitoring.

For this purpose, the derived downscaled products are used for the identification of critical hydrological states in the context of extreme event detection using classical threshold-based indicators derived from TWS climatological anomalies. The analysis discusses how improved spatial resolution may help preserve sharper anomaly structures that are otherwise smoothed at coarse scales, with possible benefits for the timely identification of critical states. The early-warning pipeline is evaluated in terms of event detection skill and timing based on the true critical states derived from the ground truth TWS in the closed-loop simulations and then extended to real GRACE data.