Abstract: In recent years, understanding and quantifying the global hydrologic cycle has become a priority research topic. Hydrologists now face the challenge to apply true data assimilation techniques to all problems where remote sensing data can provide new insights. However, this is a difficult task due to the highly nonlinear nature of land-surface processes, the size of the problem, and the lack of data and experience to determine error statistics accurately. Consequently, the implementation of data assimilation techniques always requires trade-offs between resolution, complexity, computational effort, and data availability. Our approach is based in variational data assimilation. All control theory or variational assimilation approaches perform a global time-space adjustment of the model solution to all observations and thus solve a smoothing problem. Firstly, we give a description about the site of this study and then we describe the modeling approach used to simulate the temperatures and moistures in SVAT-ICARE model. The optimisation problem is then formulated in the framework of control optimal theory, followed by a brief discussion of genetic algorithms used in the optimization algorithm.