Coastal aquifers worldwide are under threat of saltwater intrusion, in which seawater migrates into terrestrial aquifers, resulting in the loss of fresh groundwater resources. It is critical for proactive groundwater management to understand the current state and development of the salinity distribution due to saltwater intrusion. The data used to monitor the salinity distribution commonly come from boreholes, which, while useful, are limited in spatial sampling and often do not provide sufficient detail to fully capture the lateral variations in lithology and salinity seen in coastal regions experiencing saltwater intrusion. The geophysical airborne electromagnetic (AEM) method has been used increasingly to support groundwater management at the regional scale. In this thesis, I investigate the use of the AEM method to inform the modeling of saltwater intrusion. The study area for this thesis is the coastal Salinas Valley, CA, where saltwater has migrated, in some locations, over 10 km inland; the rate and inland extent of saltwater intrusion have been exacerbated by high rates of groundwater extraction to support agriculture in the area. Understanding the complex pattern of salinity and unique hydrogeology of the study area are the first steps to modeling the evolution of the salinity distribution. I analyzed AEM data, collected in the study area, to understand the distribution of fresher water and saltwater in the three principal aquifers within the study area. The distribution of fresher water in the upper two aquifers illustrates a hydrogeologic process that mitigates saltwater intrusion, while the distribution of saltwater in the lower two aquifers reveals locations where saltwater can quickly migrate downward from one aquifer to the other. I conducted a synthetic experiment with the goal of understanding the complexity of a numerical model of saltwater intrusion necessary to accurately predict the salinity distribution, as well as the information contained within acquired AEM data to inform such a model. I found a set of hydrogeologic processes and corresponding parameters that were necessary to predict the geometry and location of the transition zone, where the salinity varies from freshwater to saltwater. I found the parameters AEM data were most informative of, and also found that it was crucial to accurately capture the rock-physics relationship: the relationship between the material properties of the subsurface and the electrical resistivity, which the AEM data can be used to estimate. I developed a method to reduce the uncertainty in the rock-physics relationship. I used a machine-learning approach to improve estimates of the resistivity by incorporating auxiliary information at my study area that describes that location and aquifer corresponding to available measurements of material properties. I used AEM data to inform the modeling of saltwater intrusion in my study area, with the goal of reducing the uncertainty in the development of the salinity distribution. I applied the AEM data, acquired in my study area, to a numerical model of saltwater intrusion to reduce the uncertainty in the prediction of the future salinity distribution, and the rate of salinization, of the two lower aquifers in my study area. I found that the predicted future salinity, and the rate of salinization, in the aquifers was lower than was predicted before the AEM data were used to reduce uncertainty.