Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model

For decision support, the insights and predictive power of numerical process models can be hampered by insufficient expertise and computational resources required to evaluate system response to new stresses. An alternative is to emulate the process model with a statistical “metamodel.” Built on a dataset of collocated numerical model input and output, a groundwater flow model was emulated using a Bayesian Network, an Artificial neural network, and a Gradient Boosted Regression Tree. The response of interest was surface water depletion expressed as the source of water-to-wells. The results have application for managing allocation of groundwater. Each technique was tuned using cross validation and further evaluated using a held-out dataset. A numerical MODFLOW-USG model of the Lake Michigan Basin, USA, was used for the evaluation. The performance and interpretability of each technique was compared pointing to advantages of each technique. The metamodel can extend to unmodeled areas. [Display omitted] •Metamodeling can be used for decision support emulating groundwater models.•Artificial neural networks, gradient boosting, and Bayesian networks each have advantages.•Spatial relations among wells and streams are key drivers for source of water to groundwater wells.