Performance of AMSR_E soil moisture data assimilation in CLM4.5 model for monitoring hydrologic fluxes at global scale

Abstract In this study, we evaluated the performance of community land surface model (CLM4.5) to simulate the hydrologic fluxes, such as, soil moisture (SM), evapotranspiration (ET) and runoff with (without) remote sensing data assimilation. The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR_E) daily SM (both ascending and descending) are incorporated into the CLM4.5 model using data assimilation (DA) technique. The GLDAS data is used to validate the AMSR_E SM data and evaluate the performance of CLM4.5 simulations. The AMSR_E SM data are rescaled to meet the resolution of CLM4.5 model. By assimilating the AMSR_E SM data into the CLM4.5 model can improve the SM simulations, especially over the climate transition zones in Africa, East Australia, South South America, Southeast Asia, and East North America in summer season. The Local Ensemble Kalman Filter (LEnKF) technique improves the performance of CLM4.5 model compared to the directly substituted method. The improvement in ET and surface runoff simulations from CLM4.5 model assimilated with AMSR_E SM data shares similar spatial patterns with SM.