This study presents a method for improving groundwater storage (GWS) estimates in Thailand by integrating GRACE satellite data with a land surface model through the 3-dimensional ensemble Kalman smoother (EnKS 3D) technique.
Conducted in the northern part of the Ping River Basin, the research highlights the benefits of GRACE data assimilation in enhancing groundwater monitoring systems. By comparing model-only estimates with those adjusted through GRACE data assimilation, the study found significant improvements in GWS accuracy, which has critical implications for water resource management and climate adaptation strategies in Thailand. Future work will focus on refining product resolution and incorporating additional satellite data to support broader environmental studies.
Data assimilation integrates real-world observations with predictive models to refine estimates and improve accuracy. By statistically adjusting model states based on new data and accounting for uncertainties, this method continuously updates and enhances predictions across various fields like meteorology, hydrology, and environmental monitoring.
Our lab utilizes advanced data assimilation techniques to integrate real-time observational data with predictive models. This approach enhances our understanding of hydrological trends, water quality, and climate impacts, enabling precise insights for effective water resource management and infrastructure planning.
This study improves groundwater storage estimates in Thailand by integrating GRACE satellite data with the CABLE model. The GRACE data assimilation technique significantly enhances accuracy, offering precise insights for better water resource management and planning.