A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China

The Gravity Recovery and Climate Experiment (GRACE) satellite provides an unprecedented tool for monitoring large-scale terrestrial water storage (TWS) changes. Yet, its coarse resolution restricts its effectiveness in areas with complex hydrogeological environments, such as southwestern China. To address this limitation, we propose a novel method to improve the spatial resolution of GRACE observations. Our approach leverages a deep learning downscaling model that integrates generative adversarial networks (GANs) and transformer attention mechanisms to derive the spatial patterns of TWS variations. The model incorporates the estimated total water storage changes from GRACE and some hydrological variables—including the digital elevation model (DEM), soil moisture, evapotranspiration, temperature, and precipitation—to enhance the resolution and accuracy of GRACE data. By implementing this method, we successfully increased the spatial resolution of GRACE observations from 0.25° to 0.05°. The advanced neural network downscaling model can accurately characterize local water storage variations, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.58 to 0.92. Moreover, this model not only significantly increases the spatial resolution but also maintains the spatial distribution, offering valuable insights for regional water resources management and fostering small-scale hydrological research. The results have profound implications for sustainable water resources management and climate change assessment.