Using cross-correlation analysis, we demonstrate that flux measurements at observation locations during hydraulic tomography (HT) surveys carry nonredundant information about heterogeneity that are complementary to head measurements at the same locations. We then hypothesize that a joint interpretation of head and flux data, even when the same observation network as head has been used, can enhance the resolution of HT estimates. Subsequently, we use numerical experiments to test this hypothesis and investigate the impact of flux conditioning and prior information (such as correlation lengths and initial mean models (i.e., uniform mean or distributed means)) on the HT estimates of a nonstationary, layered medium. We find that the addition of flux conditioning to HT analysis improves the estimates in all of the prior models tested. While prior information on geologic structures could be useful, its influence on the estimates reduces as more nonredundant data (i.e., flux) are used in the HT analysis. Lastly, recommendations for conducting HT surveys and analysis are presented.