Reducing structural uncertainty in conceptual hydrological modelling in the semi-arid Andes
- 1UM2 – UMR HydroSciences Montpellier, Place E. Bataillon, 34395 Montpellier CEDEX 5, France
- 2CNRS – UMR HydroSciences Montpellier, Place E. Bataillon, 34395 Montpellier CEDEX 5, France
- 3IRD – UMR HydroSciences Montpellier, Place E. Bataillon, 34395 Montpellier CEDEX 5, France
- 4Centro de Estudios Avanzados en Zonas Áridas (CEAZA), Raúl Bitrán s/n, La Serena, Chile
Abstract. The use of lumped, conceptual models in hydrological impact studies requires placing more emphasis on the uncertainty arising from deficiencies and/or ambiguities in the model structure. This study provides an opportunity to combine a multiple-hypothesis framework with a multi-criteria assessment scheme to reduce structural uncertainty in the conceptual modelling of a mesoscale Andean catchment (1515 km2) over a 30-year period (1982–2011). The modelling process was decomposed into six model-building decisions related to the following aspects of the system behaviour: snow accumulation and melt, runoff generation, redistribution and delay of water fluxes, and natural storage effects. Each of these decisions was provided with a set of alternative modelling options, resulting in a total of 72 competing model structures. These structures were calibrated using the concept of Pareto optimality with three criteria pertaining to streamflow simulations and one to the seasonal dynamics of snow processes. The results were analyzed in the four-dimensional (4-D) space of performance measures using a fuzzy c-means clustering technique and a differential split sample test, leading to identify 14 equally acceptable model hypotheses. A filtering approach was then applied to these best-performing structures in order to minimize the overall uncertainty envelope while maximizing the number of enclosed observations. This led to retain eight model hypotheses as a representation of the minimum structural uncertainty that could be obtained with this modelling framework. Future work to better consider model predictive uncertainty should include a proper assessment of parameter equifinality and data errors, as well as the testing of new or refined hypotheses to allow for the use of additional auxiliary observations.