One of the main challenges in the application of coupled or 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 reducing uncertainty in model initialization is to run the model recursively using either a single year 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 the spin-up procedure by using a combination of model simulations and an empirical DTWT function. The methodology is examined across two distinct catchments located in a temperate region of Denmark and a semi-arid region of Australia. Our results illustrate that the hybrid approach reduced the spin-up period required for an integrated groundwater–surface water–land surface model (ParFlow.CLM) by up to 50%. To generalize results to different climate and catchment conditions, we outline a methodology that is applicable to other coupled or integrated modeling frameworks when initialization from an equilibrium state is required.
Fig. 1. The hybrid spin-up approach consists of three main steps: (1) initial ParFlow.CLM spin-up simulations based on an arbitrary
DTWT distribution, (2) a state-updating step by developing a DTWT function based on percentage changes in mean annual DTWT in initial
spin-up simulations, and (3) stage 2 of ParFlow.CLM spin-up simulations until the desired equilibration level is reached.
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Last updated 29 November 2013