Conceptual hydrological modelling has traditionally been plagued by calibration difficulties due to the roughness and complex shape of objective functions. These problems led to the abandonment of powerful classical analysis methods (Newton-type optimisation, derivative-based uncertainty analysis) and have motivated extensive research into nonsmooth optimisation and even new parameter estimation philosophies (e.g. GLUE). This paper shows that some of these complexities are not inherent features of hydrological models, but are numerical artefacts due to model thresholds and poorly selected time stepping schemes. We present a numerically robust methodology for implementing conceptual models, including rainfall-runoff and snow models, that ensures micro-scale smoothness of objective functions and guarantees macro-scale model stability. The methodology employs robust and unconditionally stable time integration of the models, complemented by careful threshold smoothing. A case study demonstrates the benefits of these techniques.