Data assimilation (DA) provides an estimation framework to characterize the states of a system by merging information in measurements and physical models. The main purpose of this project is to explore the use of DA methods in designing and maximizing the information content of sensor networks deployed in a complex environmental system. The application is a test bed for wastewater reuse for irrigation in Palmdale, CA. In this study, synthetic experiments with the Ensemble Kalman Filter (EnKF) are performed to estimate soil moisture profiles under three uncertainty scenarios: 1) Initial condition uncertainty; 2) Initial condition and irrigation rate uncertainty; and 3) Initial condition, irrigation rate and time-invariant parameter uncertainty. The estimates from the EnKF with data from embedded sensors are compared with the performance of an open-loop forward modeling simulation and show significant improvements in the estimate of system states under all uncertainty scenarios.