We systematically evaluated the effect of model complexity and calibration strategy on estimated recharge using four varyingly complex models and a unique long-term recharge data set. A differential split sample test was carried out by using six calibration periods with climatically contrasting conditions in a constrained Monte Carlo approach. All models performed better during calibration than during validation due to differences in model structures and climatic conditions. The two more complex, physically-based models predicted the observed recharge with relatively small uncertainties, even when calibration and prediction periods had different climatic conditions. In contrast, the more simplistic soil-water balance model significantly underestimated the recharge rates. The fourth, semi-mechanistic model captured the observed recharge rates, but with a larger uncertainty range than the physically-based models. Our results may have relevant implications for a broad range of applications when recharge models are used as decision-making tools.
Environmental Modelling & Software – Elsevier
Published: May 1, 2018
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