Data assimilation of soil moisture and leaf area index effectively improves the simulation accuracy of water and carbon fluxes in coupled farmland hydrological model

Real time status of farmland hydrology and crop growth is essential for agricultural management. Data assimilation is a common method to improve the prediction accuracy of the model by fusing observed and simulated data. For the farmland hydrological processes, evapotranspiration (ET) and net ecosystem productivity (NEP) are widely concerned and strongly affected by crop growth and soil moisture. In this study, data assimilation for soil water content (SWC) and leaf area index (LAI) was combined with a coupled farmland hydrological model, and the potential of Kalman filter (KF) and ensemble Kalman filter (EnKF) methods to enhance model accuracy were explored. Furthermore, the impact of observation density of assimilated data and different assimilation strategies (single-factor or dual-factor assimilation) were analyzed. The findings revealed that both KF and EnKF methods effectively improved the simulation ability of SWC and LAI. When assimilation was performed daily, KF could obtain results comparable to EnKF with assimilation efficiency coefficient (Eff) exceeded 70%. However, with a reduced assimilation frequency for LAI to ten-day interval, EnKF exhibited superior applicability, demonstrating a 13% increase in Eff. The assimilation of soil moisture could positively affect the simulation results of ET with Eff close to 10%, and the assimilation of LAI could improve the simulation accuracy of NEP with Eff close to 15%. Overall, dual-factor assimilation proved to have a more substantial impact than single-factor, even reducing the frequency to ten-day interval. The sensitivity analysis showed that the coupling model could resist the influence of the preset observation error in the filter, with data assimilation effectively mitigating the influence of parameter errors in coupling model. These analyses supply an effective basis to deepen the understanding of improve real time simulation accuracy of farmland hydrological model with data assimilation.