Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 14 2 2010 10.5194/hess-14-171-2010 http://www.hydrol-earth-syst-sci.net/14/171/2010/ http://www.hydrol-earth-syst-sci.net/14/171/2010/hess-14-171-2010.html http://www.hydrol-earth-syst-sci.net/14/171/2010/hess-14-171-2010.pdf 171 192 2010-02-02 Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile R. Rojas rodrigo.rojas@jrc.ec.europa.eu O. Batelaan L. Feyen A. Dassargues Applied geology and mineralogy, Department of Earth and Environmental Sciences, Katholieke Universiteit Leuven, Celestijnenlaan 200E, 3001 Heverlee, Belgium Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium Land management and natural hazards unit, Institute for Environment and Sustainability (IES), Joint Research Centre (JRC), European Commission, Via E. Fermi 2749, TP261, 21027 Ispra (Va), Italy Hydrogeology and Environmental Geology, Department of Architecture, Geology, Environment, and Constructions (ArGEnCo), Université de Liège, B.52/3 Sart-Tilman, 4000 Liège, Belgium now at: Land management and natural hazards unit, Institute for Environment and Sustainability (IES), Joint Research Centre (JRC), European Commission (EC), Via E. Fermi 2749, TP261, 21027 Ispra (Va), Italy 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 <b><i>M</i></b> 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 <b><i>D</i></b>. GLUE-based cumulative predictive distributions for each member of <b><i>M</i></b> 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. 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