Role of model parameterization in risk-based decision support: An empirical exploration

•A Bayesian paired model and decision analysis approach is adopted.•Two case studies empirically demonstrate the potential damage caused by history matching models with reduced parameterization schemes.•Even “highly parameterized” schemes may suffer from ill-effects such as bias.•Ill-effects may be mitigated by adopting a prior uncertainty stance (i.e., by avoiding “calibration”).•Differencing simulated outputs of spatially-integrated nature may also provide protection against these ill-effects. The degree with which to parameterize a computer model that is to be used for risk-based resource management decision support has been a topic of much discussion in the environmental modeling industry, and remains a difficult choice facing practitioners. High-dimensional parameterization schemes allow for a more robust expression of model input uncertainty over traditional lower-dimensional schemes, but often incur a higher computational burden and require greater understanding of inverse problem theory to implement effectively. However, a number of significant questions remain, such as: “What level of parameterization is needed to adequately express uncertainty for a given decision-relevant simulated output?”; and “To what extent can a simplified parameterization be adopted while maintaining the ability of the model to serve as a decision-support tool?”. This study addresses these questions, among others, by using empirical paired complex-simple model analyses to investigate the consequences of reduced parameterization on decision-relevant simulated outputs in terms of bias incursion and underestimation of uncertainty. A Bayesian decision analysis approach is adopted to facilitate evaluation of parameterization reduction outcomes, not only in terms of the prior and posterior probability density functions of decision-relevant simulated outputs, but also in terms of the management decisions that would be made on their basis. Two integrated surface water/groundwater model case study examples are presented; the first is a complex synthetic model used to forecast groundwater abstraction-induced changes in ecologically-sensitive streamflow characteristics, and the second is a real-world regional-scale model (Hauraki Plains, New Zealand) used to simulate nitrate-loading impacts on water quality. It is shown empirically that, for some decision-relevant simulated outputs, even relatively high-dimensional parameterization schemes ( > 2,000 adjustable parameters) display sig