estconvs2s.zip

The GRACE and GRACE-FO satellite missions play a critical role in helping us understand changes in water storage, including groundwater levels, which is crucial for managing water resources effectively. However, a gap between the data collected by these two missions poses challenges in making accurate predictions about water storage. To address this gap, we developed a new model called Enhanced Spatiotemporal Convolutional Sequence to Sequence Network (ESTConvS2S). This model leverages advanced deep learning techniques to fill in missing data and improve the accuracy of water storage predictions. Our study focused on Southwest China, a region known for its unique karst topography and diverse climate conditions, making it particularly sensitive to water storage changes. The ESTConvS2S model showed high accuracy in estimating water storage dynamics. We validated the model by comparing its predictions with actual groundwater measurements and observed a strong correlation, underscoring the reliability of the model. Our model not only effectively bridges the data gap between GRACE and GRACE-FO missions but also significantly enhances our ability to estimate groundwater data accurately. This improvement is vital for better water management, especially in regions facing water scarcity or excessive groundwater extraction.