We use sensitivity analysis to identify the parameters that are most responsible for controlling land surface model (LSM) simulations and to understand complex parameter interactions in three versions of the Noah LSM: the standard version (STD), a version enhanced with a simple groundwater module (GW), and version augmented by a dynamic phenology module (DV). We use warm season, high-frequency, near-surface states and turbulent fluxes collected over nine sites in the U. S. Southern Great Plains. We quantify changes in the pattern of sensitive parameters, the amount and nature of the interaction between parameters, and the covariance structure of the distribution of behavioral parameter sets. Using Sobol's total and first-order sensitivity indexes, we show that few parameters directly control the variance of the model response. Significant parameter interaction occurs. Optimal parameter values differ between models, and the relationships between parameters also change. GW decreases unwarranted parameter interaction and appears to improve model realism, especially at wetter study sites. DV increases parameter interaction and decreases identifiability, implying it is overparameterized and/or underconstrained. At a wet site, GW has two functional modes: one that mimics STD and a second in which GW improves model function by decoupling direct evaporation and base flow. Unsupervised classification of the posterior distributions of behavioral parameter sets cannot group similar sites based solely on soil or vegetation type, helping to explain why transferability between sites and models is not straightforward. Our results suggest that the a priori assignment of parameters should also consider the climatic conditions of a study location.