Forest landscape simulation models (FLM) have been extensively used for projecting ecosystem dynamics and carbon fluxes. However, more guidance and methods for formal calibration and corroboration of FLMs are needed to ensure higher fidelity results from these models. We developed a novel systematic methodology for calibrating and corroborating a FLM at the grid-cell scale using empirical estimates from the national forest inventory in the United States (US) which uses an equal probability sample design.We illustrate our approach by using the Forest Inventory and Analysis (FIA) data from the US Department of Agriculture Forest Service across the state of Wisconsin to represent initial site conditions and calibrate parameters for the Density Succession extension of the LANDIS-II model, formally coupled with an iterative model corroboration stage focused on the growth and internal competition component of the model at a site-scale using plot remeasurement data that span 20 years. We used a formal equivalence testing approach to compare model predictions to empirical estimates for permanent sample plots.We found that the model performed well, with 21 out of 30 species demonstrating equivalence in the estimator used to characterize basal area following initial parameterization. Upon calibration of the estimator, the nine species not initially equivalent all showed reduced bias, with five of the species ultimately passing the equivalence test. The species that did not achieve equivalence through calibration were generally the least abundant ones.These results have increased our confidence that the Density Succession algorithms were well implemented within the model and provides a useful additional succession option within the LANDIS-II framework. We believe this methodology can capture a wider range of local scale variability compared to previous methods conducted at a landscape level using summary statistics. The iterative nature of the method also gives an opportunity for learning about the model components and the reference plot data. Our approach will help researchers using landscape simulation models to follow a replicable framework for corroborating their results and prioritize forest management activities and land use planning, which can help reduce external pressures on forests and help mitigate climate change effects.