Land data assimilation is a progressive area in hydrology. In order to ensure better predictability of land surface conditions (heat and moisture fluxes), and atmospheric conditions it is necessary to have physical models that are true representations of reality. Therefore, it is necessary to have data integration/assimilation of all hydrological variables that are observed at/near the land surface. These variables are (and not limited to): soil moisture, surface temperature, and streamflow. In this paper, I will outline a very general methodology to (a) evaluate sensitivity and contributions of each variables in the flow process (b) identify simple ways of nudging the model as oppossed to a full-blown kalman (or any other) filtering scheme. The philosophy of this approach is a physical adjustment of the state and derived variables to maintain consistency in the water and the energy budget equations.