Abstract. In this work we assess the uncertainty in modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) -North Chile using a novel and fully integrated multi-model approach aimed at explicitly accounting for uncertainties arising from the definition of alternative conceptual models. The approach integrates the Generalized Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods. For each member of an ensemble M of potential conceptualizations, model weights used in BMA for multi-model aggregation are obtained from GLUE-based likelihood values. These model weights are based on model performance, thus, reflecting how well a conceptualization reproduces an observed dataset D. GLUEbased cumulative predictive distributions for each member of M are then aggregated obtaining predictive distributions accounting for conceptual model uncertainties. For the PTA we propose an ensemble of eight alternative conceptualizations covering all major features of groundwater flow models independently developed in past studies and including two recharge mechanisms which have been source of debate for several years. Results showed that accounting for heterogeneities in the hydraulic conductivity field (a) reduced the uncertainty in the estimations of parameters and state variables, and (b) increased the corresponding model weights used for multi-model aggregation. This was more noticeable when the hydraulic conductivity field was conditioned on available hydraulic conductivity measurements. ContriCorrespondence to: R. Rojas (rodrigo.rojas@jrc.ec.europa.eu) bution of conceptual model uncertainty to the predictive uncertainty varied between 6% and 64% for ground water head estimations and between 16% and 79% for ground water flow estimations. These results clearly illustrate the relevance of conceptual model uncertainty.

Abstract. In this work we assess the uncertainty in modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) – North Chile using a novel and fully integrated multi-model approach aimed at explicitly accounting for uncertainties arising from the definition of alternative conceptual models. The approach integrates the Generalized Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods. For each member of an ensemble M of potential conceptualizations, model weights used in BMA for multi-model aggregation are obtained from GLUE-based likelihood values. These model weights are based on model performance, thus, reflecting how well a conceptualization reproduces an observed dataset D. GLUE-based cumulative predictive distributions for each member of M are then aggregated obtaining predictive distributions accounting for conceptual model uncertainties. For the PTA we propose an ensemble of eight alternative conceptualizations covering all major features of groundwater flow models independently developed in past studies and including two recharge mechanisms which have been source of debate for several years. Results showed that accounting for heterogeneities in the hydraulic conductivity field (a) reduced the uncertainty in the estimations of parameters and state variables, and (b) increased the corresponding model weights used for multi-model aggregation. This was more noticeable when the hydraulic conductivity field was conditioned on available hydraulic conductivity measurements. Contribution of conceptual model uncertainty to the predictive uncertainty varied between 6% and 64% for ground water head estimations and between 16% and 79% for ground water flow estimations. These results clearly illustrate the relevance of conceptual model uncertainty.