Complexity versus simplicity: an example of groundwater model ranking with the Akaike Information Criterion

Abstract

Abstract. A groundwater model characterized by a lack of field data to estimate hydraulic model parameters and boundary conditions combined with many piezometric head observations was investigated concerning model uncertainty. Different conceptual models with a stepwise increase from 0 to 30 adjustable parameters were calibrated using PEST. Residuals, sensitivities, the Akaike Information Criterion (AIC), and the likelihood of each model were computed. As expected, residuals and standard errors decreased with an increasing amount of adjustable model parameters. However, the model with only 15 adjusted parameters was evaluated by AIC as the best option with a likelihood of 98%, while the uncalibrated model obtained the worst AIC value. Computing of the AIC yielded the most important information to assess the model likelihood. Comparing only residuals of different conceptual models was less valuable and would result in an overparameterization of the conceptual model approach. Sensitivities of piezometric heads were highest for the model with five adjustable parameters reflecting also changes of extracted groundwater volumes. With increasing amount of adjustable parameters piezometric heads became less sensitive for the model calibration and changes of pumping rates were no longer displayed by the sensitivity coefficients. Therefore, when too many model parameters were adjusted, these parameters lost their impact on the model results. Additionally, using only sedimentological data to derive hydraulic parameters resulted in a large bias between measured and simulated groundwater level.