Groundwater recharge predictions in contrasted climate: The effect of model complexity and calibration period on recharge rates

Groundwater recharge predictions in contrasted climate: The effect of model complexity and... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Modelling & Software Elsevier

Groundwater recharge predictions in contrasted climate: The effect of model complexity and calibration period on recharge rates

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
1364-8152
eISSN
1873-6726
D.O.I.
10.1016/j.envsoft.2018.02.005
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Environmental Modelling & SoftwareElsevier

Published: May 1, 2018

References

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