The stabilities of rehabilitated landforms are often threatened by the altered surface hydrology, especially the excessive surface run-off, which may cause severe water erosion and associated pollutant transport to surrounding areas. These effects would lead to pollution in waterways and render the failure of newly established vegetation. An accurate quantitative evaluation of run-off problems are consequently essential for the proper rehabilitation designs which can help minimise these harmful consequences. For this purpose, in this study a novel run-off model, RunCA, was developed based on Cellular Automata (CA), integrating fundamental hydraulic principles. In this model, a series of rectangular cells were employed to cover the study area. Run-off production within each cell was simulated by determining the cell state (water surface elevation) that consists of both cell altitude and water depth. The distribution of water flow among cells was determined by applying CA transition rules based on the improved minimisation-of-difference algorithm and the calculated flow travelling time. RunCA was applied to various aspects in mined land rehabilitation planning, including landform designs for both hill-slopes and tailings dams, revegetation strategies and cover constructions. Simulation results showed that surface run-off increased with slope gradient and more run-off was produced on convex and straight slopes than on concave and S-shape slopes. Both mounds and rip lines constructed on the tailings dam surface could help reduce the water discharge. Vegetation planted in rows were more effective than those in columns or patches in controlling run-off. Increased cover thickness had the potential in reducing run-off rates, and the covers with an increasing thickness from top to bottom of the slope tended to be more efficient than those with a constant thickness. All these results have demonstrated the efficacy of RunCA as a simulation tool in assessing the hydrologic behaviours of different rehabilitation designs. Compared to the traditional hydrologic models, most of which were developed for the catchment scale studies, RunCA has the potential advantages in capturing the dynamic and spatially varied run-off behaviours at different spatial and temporal scales.CITATION:Shao, Q,Baumgartl, T, Huang, L and Weatherley, D, 2014. A cellular automata-based run-off model and its application in mined land rehabilitation designs, in Proceedings Life-of-Mine 2014 , pp 81-96 (The Australasian Institute of Mining and Metallurgy: Melbourne).