Bayesian calibration of groundwater models with input data uncertainty. Issue 4 (19th April 2017)

• Record Type: Journal Article
• Authors: Xu, Tianfang; Valocchi, Albert J.; Ye, Ming; Liang, Feng; Lin, Yu‐Feng
• Is Part Of: Water resources research. Volume 53:Issue 4(2017)
• Page Start: 3224
• Page End: 3245
• Publication Date: 2017-04-19
• Subjects: input uncertainty -- calibration -- Bayesian -- uncertainty quantification; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2016WR019512
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Effective water resources management typically relies on numerical models to analyze groundwater flow and solute transport processes. Groundwater models are often subject to input data uncertainty, as some inputs (such as recharge and well pumping rates) are estimated and subject to uncertainty. Current practices of groundwater model calibration often overlook uncertainties in input data; this can lead to biased parameter estimates and compromised predictions. Through a synthetic case study of surface‐ground water interaction under changing pumping conditions and land use, we investigate the impacts of uncertain pumping and recharge rates on model calibration and uncertainty analysis. We then present a Bayesian framework of model calibration to handle uncertain input of groundwater models. The framework implements a marginalizing step to account for input data uncertainty when evaluating likelihood. It was found that not accounting for input uncertainty may lead to biased, overconfident parameter estimates because parameters could be over‐adjusted to compensate for possible input data errors. Parameter compensation can have deleterious impacts when the calibrated model is used to make forecast under a scenario that is different from calibration conditions. By marginalizing input data uncertainty, the Bayesian calibration approach effectively alleviates parameter compensation and gives more accurate predictions in the synthetic case study. The marginalizing Bayesian method also decomposes prediction uncertainty into uncertainties contributed by parameters, input data, and measurements. The results underscore the need to account for input uncertainty to better inform postmodeling decision making. Key Points: Neglecting uncertainty in inputs such as pumping and recharge rates may undermine the prediction power of calibrated groundwater models A marginalizing Bayesian method accounts for input uncertainty and yields more accurate prediction for a synthetic case study Based on variance decomposition analysis, input uncertainty can be the dominant source of prediction uncertainty