BBAuthors: Zha, Y.; Advisor: -; Participants: Zhu, P.; Zhang, Q.; Mao, W.; Shi, L. (2019)
In this study, three classical data assimilation methods, i.e., the ensemble Kalman filter (EnKF), the ensemble randomized maximum likelihood filter (EnRML), and the Markov chain Monte Carlo (MCMC), were investigated numerically in terms of the utility to cope with three different types of observations. Results show that, compared with the EnKF approach, EnRML is a superior method to extract the parameter information from observations. The MCMC approach performs well in homogeneous soil but not in heterogeneous soil. Regardless of the data assimilation methods and the soil heterogeneity, point‐scale soil water pressure head data are the most valuable in terms of soil parameter estimat... 
