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This paper discusses the optimization of monitoring data for the increased reliability of regional groundwater models and the predictions that depend on them. The significant costs of commissioning and maintaining groundwater monitoring networks are such that there is great benefit in being able to assess where data gathering has the greatest impact on improving predictive reliability. This optimization assessment can be made on the basis of existing networks or prior to any data acquisition efforts. Various data acquisition strategies, for quite disparate data types, can be compared in terms of their ability to increase the reliability of model based predictions; data collection strategies which provide the greatest return for investment can then be selected for implementation. Similarly the relative merits of making measurements at different locations and times can be assessed. Using the Lockyer Valley ground water model (RPS 2010) we demonstrate how predictive uncertainty analysis can provide a powerful foundation for optimizing both existing monitoring networks and future data acquisition strategies to support model based environmental management. Such analyses are efficient yet robust. The particular characterization of model predictive variance in the problem formulation employed (Moore and Doherty, 2005), ensures that the contributions to predictive uncertainty by both measurement errors and environmental heterogeneity that cannot be captured by the calibration process is accounted for in the analysis. Efficiency is gained via a linearity assumption in the equation used in the analysis, which allows the calculation to be made sufficiently rapidly, so that it can be repeated at many alternative existing or proposed monitoring sites and times. Furthermore, this analysis has no cost barriers, as the software for such analyses is in the public domain (Doherty, 2011a and b). These are particularly important benefits in the large scale regional model context, where monitoring is typically a significant effort and is subject to public scrutiny in terms of both cost and rigour.

Optimisation of monitoring data for increased predictive reliability of regional water allocation models