Coupled effects of observation and parameter uncertainty on urban groundwater infrastructure decisions

Abstract

Urban groundwater management requires complex environmental models to represent interactions between hydrogeological processes and infrastructure systems. While the impacts of external uncertainties, such as climate and population growth, have been widely studied, there is limited understanding of how decision support is altered by endogenous uncertainties arising from model parameters and observations used for calibration. This study investigates (1) the importance of observation choice and parameter values on aquifer management objectives when controlling for model error and (2) how the relative performance of management alternatives varies when exposed to endogenous uncertainties, both individually and in combination. We use a spatially distributed groundwater model of the Valley of Mexico, where aquifer management alternatives include demand management, targeted infiltration, and wastewater reuse. The effects of uncertainty are evaluated using global sensitivity analysis, performance ranking of alternatives under a range of human–natural parameters, and identification of behavioral parameter sets filtered with an error metric calculated from varying subsets of observations. Results show that the parameters governing hydraulic conductivity and total water use in the basin have the greatest effect on management objectives. Error metrics (i.e., squared residuals of piezometric head) are not necessarily controlled by the same parameters as the head-based objectives needed for decision-making. Additionally, observational and parameter uncertainty each play a larger role in objective variation than the management alternatives themselves. Finally, coupled endogenous uncertainties have amplifying effects on decision-making, leading to larger variations in the ranking of management alternatives than each on their own. This study highlights how the uncertain parameters of a physically based model and their interactions with uncertain observations can affect water supply planning decisions in densely populated urban areas.