One of the main challenges in catchment scale application of coupled/integrated hydrologic models is associated with specifying 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 (spin-up) until the system equilibrates with respect to the state and diagnostic variables. However, the spin-up approach requires many years of simulations that can be computationally intensive. In this study, a new hybrid approach was developed to reduce the computational time of an integrated groundwater-surface water-land surface model named ParFlow.CLM during spin-up. We evaluated the applicability of the hybrid approach
using the Skjern River ParFlow.CLM model developed over a 208 km2 area. The Skjern River catchment situated in the western Denmark and has a temperate climate with agricultural lands covering 78% of the catchment's area. Our results illustrated that the hybrid approach reduced the spin-up time of ParFlow.CLM in the order of 40 to 50% depending on the soil moisture re-initialization method. Water balance analysis during the spin-up further illustrated the sensitivity of surface water-groundwater exchange to model initializations and highlighted the importance of spin-up in determination of equilibrium state. This methodology is also applicable to other coupled/integrated modelling platforms.
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Last updated 31st January 2013