Multivariate calibration can increase simulated discharge uncertainty and model equifinality

Abstract. Hydrological models are typically calibrated against discharge data. However, the resulting parameterization does not necessarily lead to a realistic representation of other variables, and multivariate model calibration may overcome this limitation. It seems reasonable to expect that multivariate calibration leads to reduced hydrograph uncertainty, associated with more constrained flux maps (i.e., combinations of streamflow generation mechanisms). However, this expectation assumes that an intersection exists within the parameter space between the separate behavioural clouds of the two or more variables considered in multivariate calibration. Here, we tested this assumption in twelve Australian catchments located in five different climate zones. We calibrated the SIMHYD model using a Monte Carlo-based approach in which an initially large sample of parameter sets was constrained using discharge only, actual evapotranspiration only, and a combination of both variables (i.e., a single combined objective function). We demonstrated that adding actual evapotranspiration to a discharge-based calibration caused hydrograph uncertainty to increase for 11 of the 12 sites. Similarly, flux maps became less constrained under multivariate calibration relative to univariate calibration. Analysis of behavioural parameter sets suggests that these symptoms could be caused by non-overlapping behavioural parameter distributions among the different variables. By separately considering both locally observed and remote sensing-based evapotranspiration in the analysis, we could demonstrate that the source of the information did not affect our findings. This has implications both for model parameterization and model selection, emphasising that the value of non-discharge data in calibration is contingent on the suitability of the model structure.