Improving JULES Soil Moisture Estimates through 4D-En-Var Hybrid Assimilation of COSMOS-UK Soil Moisture Observations

Abstract. Accurate soil moisture estimates are essential for effective management and operational planning in various applications, including flood and drought response. However, soil moisture values derived from land surface models often exhibit significant deviations from in-situ observations. Data assimilation combines model information with observations to enhance prediction accuracy. Previous studies have typically focused on either estimating the initial soil moisture state or optimizing Pedotransfer Function (PTF) constants, which link soil texture to the hydraulic properties of the land surface models. In contrast, in this study, we employ a novel approach by performing joint state-parameter assimilation for the JULES model. We optimized both the PTF constants and the initial soil moisture conditions simultaneously. Using Four-Dimensional Ensemble Variational hybrid data assimilation, we ingested field-scale soil moisture observations from the Cosmic-ray Soil Moisture Monitoring Network across 16 diverse sites in the UK. The results demonstrate that joint state-parameter assimilation significantly enhances the accuracy of soil moisture estimates, improving the average Kling Gupta Efficiency values from 0.33 to 0.72 across different soil characteristics. These findings indicate that our proposed joint state-parameter assimilation framework holds great potential for enhanced predictive accuracy in land surface models.