Abstract: Groundwater level is a low-cost measurement that may contain valuable information about unsaturated flow. To provide an insight into the value of groundwater level data, the ensemble Kalman filter for one-dimensional saturated-unsaturated flow is developed to assimilate the dynamic groundwater level data. The potential value of groundwater level data is investigated in a series of synthetic numerical experiments. Results show that when assimilating the groundwater level data alone, the model parameters and state variables give better estimations of the pressure head distribution when updated simultaneously than when only the state variables are updated. Groundwater level data leads to satisfactory estimations if one of the soil hydraulic parameters in the multi-layer soil is identified. However, groundwater level data cannot produce optimal (unique) estimates if multiple parameters are determined simultaneously because of the complicated relationships between the groundwater level data and other parameters. Groundwater level is a source of supplementary information that, when jointly used with soil water content data, can be used to improve parameter estimations and predictions of soil moisture content.