DataSheet1_Complex or Simple—Does a Model Have to be One or the Other?.DOCX

The primary tasks of decision-support modelling are to quantify and reduce the uncertainties of decision-critical model predictions. Reduction of predictive uncertainty requires assimilation of information. Generally, this information resides in two places: 1) expert knowledge emerging from site characterization and 2) field measurements of present and historical system behavior. The former is uncertain and should therefore be expressed stochastically in a model. The range of parameter and predictive possibilities can then be constrained through history-matching. Implementation of these Bayesian principles places conflicting demands on the level of model structural complexity. A high level of structural complexity can facilitate expression of expert knowledge by establishing model details that are recognizable by site experts, and through supporting model parameters that bear a close relationship to real-world hydraulic properties. However, such models often run slowly and are numerically delicate; history-matching therefore becomes difficult or impossible. In contrast, if endowed with enough parameters, structurally simple models facilitate the achievement of a good fit between model outputs and field measurements. However, the values with which parameters are endowed may bear a looser relationship with real-world properties and are therefore less receptive to information born of expert knowledge. The model design process is therefore one of compromise. In this paper we describe a methodology that reduces the cost of compromise by allowing expert knowledge of system properties to inform the parameters of a structurally simple model. The methodology requires the use of a complementary model of strategic, but not excessive, structural complexity that is stochastic, fast-running and requires no history-matching. We demonstrate the approach using a real-world case in which modelling is used to support management of a stressed coastal aquifer. We empirically validate the approach using a synthetic model.