One of the main challenges in catchment scale application of coupled/integrated hydrologic models is specifying a catchment's initial conditions in terms of soil moisture and depth to water table (DTWT) distributions. One approach to reduce uncertainty in model initialization is to run the model recursively using a single or multiple years of forcing data until the system equilibrates with respect to state and diagnostic variables. However, such "spin-up" approaches often require many years of simulations, making them computationally intensive. In this study, a new hybrid approach was developed to reduce the computational burden of spin-up time for an integrated groundwater-surface water-land surface model (ParFlow.CLM) by using a combination of ParFlow.CLM simulations and an empirical DTWT function. The methodology is examined in two catchments located in the temperate and semi-arid regions of Denmark and Australia respectively. Our results illustrate that the hybrid approach reduced the spin-up time required by ParFlow.CLM by up to 50%, and we outline a methodology that is applicable to other coupled/integrated modelling frameworks when initialization from equilibrium state is required.