We have tested the inverse modeling approach to derive macroscopic water stress parameters (MWSP) using different types of information, such as soil water content, pressure head, and transpiration rate. This testing was performed by numerical experiments considering a multilayered soil growing grapevines under three different irrigation regimes and two contrasted water stress scenarios. The results indicate that measurements of the soil water content alone do not contain enough information to estimate MWSP. Nonuniqueness is likely to occur, and the MWSP estimates may contain large uncertainties. However, the incorporation of only transpiration measurements into the objective function does allow the deriving of accurate MWSP. This contrast is mainly due to the difference of sensitivity to the MWSP, which is much higher for the transpiration than for the soil water content. Results obtained using only soil water pressure head measurements are similar to or poorer than those obtained using transpiration data. Moreover, visual inspection of response surfaces of the objective function suggests that the incorporation of further information in addition to transpiration into the objective function is not of great value for the identification of MWSP. Uncertainties for the MWSP estimated using the three types of information combined are in most cases only 1.3 times smaller than when transpiration measurements alone are incorporated into the objective function. Beyond the specific results obtained for the estimation of MWSP we find that the parameters estimates and their associated uncertainties are strongly dependent upon the type, quantity, and quality of the information included into the objective function. Hence inverse modeling may provide a means to design better experiments.