Hemmings B, Knowling MJ, Moore CR. 2019. Assessing the uncertainty of water quality and quantity predictions made using complex regional models: Ruamāhanga North case study. Lower Hutt (NZ): GNS Science. 80 p. (GNS Science report; 2019/29). doi:10.21420/J32Q-W248.
The Smart models for Aquifer Management research programme aims to explore the utility and implications of using simple numerical models for decision support applications. This report details a case study example of the application of a complex numerical groundwater model as a tool of assessing the effectiveness of land- and water-use scenarios. The case study focuses on the upper Ruamāhanga catchment, Greater Wellington, NZ (Ruamāhanga North). The effect of land- and water-use scenarios are assessed relative to desired surface and ground water quality and surface water flow outcomes. Scenarios relate to water abstraction and nitrate loading changes related to distributed changes in land-use. Core to the demonstrated use of the numerical model for decision support is the application of probabilistic uncertainty analysis. Prior parameter uncertainty derived from expert knowledge and upstream modelling efforts is propagated to simulated outputs (predictions). Parameters are conditioned through the assimilation of observation data in a truncated calibration exercise which also informs an estimation of the posterior parameter uncertainty. Through comparing the prior and posterior parameter uncertainties we highlight the apparent capacity of the observation data to inform model parameters. Propagating parameter uncertainties to predictions we explore the value of the complex regional model and the merit of model calibration in decision support modelling. (auth)