Quantitative analyses of empirical data requirements for hydrological simulations are rare. This study aims to analyze how a multi-objective optimization framework and information content computations aid in quantifying field-scale data worth in drainage studies. The results showed how a 1D numerical model and a differential evolution algorithm performed in describing the field water balance. The choice of the optimization target (subsurface drain discharge and surface runoff) impacted the simulation results more than parameter deviations. While the information content of surface runoff data was higher than that of drain discharge, drain discharge data contained more information on most of the soil parameters. Uncertainties related to groundwater outflow data, which were not used in the optimization, were higher than those of drain discharge and surface runoff. A central weighing optimization scheme with two data types produced the best but still incomplete description of the field hydrology. Despite the modest model performance, the results demonstrated how the choice of empirical data and optimization strategy can lead to uncertainties in drainage simulations and how the uncertainties can be assessed. Practically, a low amount of information and a parameter sensitivity analysis can lead to a biased description of uncertainty related to such hydrological variables which are not used in the optimization. Benefits of the modeling framework were shown when assessing (1) model structure adequacy with the Pareto front analysis, (2) information content of different data types regarding different parameters, and (3) uncertainties related to simulating hydrological variables based on optimization against a given data type.