<p><strong>Abstract.</strong> Soil water movement is quite important due to its direct effects on environment, agriculture and hydrology. Simulation of soil water movement requires accurate determination of model parameters as well as initial and boundary conditions. However, it is difficult to obtain the accurate initial soil moisture/matric potential profile at the beginning of simulation time, making it necessary to run the simulation model from arbitrary initial condition until the

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... ty of initial condition (UIC) is diminished. The behavior of this process is usually defined as "warming up". In this paper, two common methods in quantifying the UIC (one is based on running a single simulation recursively across multiple hydrological years, and the other is based on Monte-Carlo simulations with various initial condition) are compared and the required "warm-up" time twu (minimum time required for model to warm up to eliminate the UIC) is identified with different soil textures, meteorological conditions, and soil profile lengths. Then we analyze the effects of different initial conditions on parameter estimation within two data assimilation frameworks (i.e, ensemble Kalman filter and iterative ensemble smoother), and assess several existing model initializing methods to obtain initial condition based on the availability of data related to the retrieval of initial soil moisture profile. Results reveal that Monte-Carlo simulations and the recursive simulation over many years can both demonstrate the temporal behavior of UIC and a common threshold is recommended to determine the warm-up time for both methods. Besides, the relationship between warm-up time for variably saturated flow modeling and the model settings (soil textures, meteorological conditions and soil profile length) are quantitatively identified. In addition, we propose a "warm-up" period before assimilating data in order to obtain a better performance of parameter and state estimation.</p>