A Self-Calibration Variance-Component Model for Spatial Downscaling of GRACE Observations Using Land Surface Model Outputs
High-resolution data of total water storage play a key role in assessing trends and availability of water resources. This study presents an iterative adjustment method based on the Self-calibration Variance-Component Model (SCVCM) for spatially downscaling GRACE-derived Total Water Storage Anomaly (GRACE TWSA) from its original coarse resolution (∼300 km) to a high resolution (5 km) through integrating Land Surface Model (LSM) simulated high-resolution Terrestrial Water Storage Anomaly (LSM TWSA). The proposed method takes the GRACE TWSA and the LSM TWSA, which includes soil water content, snow water equivalent, and plant water, as inputs with unknown uncertainties. It then establishes an observation system to estimate the unknown TWSA at the high resolution through an iterative adjustment process based on a posteriori variance-component estimation technique. By applying the method to the coarse-resolution (∼300 km) GRACE TWSA from the JPL (Jet Propulsion Laboratory) mascon solution and the high-resolution (5 km) LSM TWSA from the Ecological Assimilation of Land and Climate Observations (EALCO) model, we evaluated its benefit and effectiveness. The results show that the proposed method is capable to downscale GRACE TWSA with improved uncertainties. The downscaled GRACE TWSA are also evaluated through in situ groundwater monitoring well observations and the results show a certain level agreement between the estimated and observed trends.