This paper presents an optimum parameter curve as a visualisation of all the possible local and global peaks on the multidimensional response surface of the objective function associated with the calibration of hydrological models, coupled with an auxiliary curve called the search step index curve to facilitate analysis of the shape of the response surface. The construction of such an optimum parameter curve to analyse the response surface of a parametric model (conceptual or black-box) is directly prompted by the concept of equifinality embodied in the GLUE methodology of Beven and Binley, i.e. that there may be many parameter sets equally acceptable, in terms of near-equal model efficiency values, as simulators of the response of a watershed to rainfall. With the suggested methods, several numerical experiments have been conducted, firstly for a 2-parameter black-box model (the Gamma function model), using synthetic error-free data for selected sets of parameter values, and secondly for a typical primitive conceptual rainfall–runoff model (the 9-parameter Wuhan-01 model), using the real data of three catchments. It is deduced that the goodness of the optimum parameter set estimated by optimisation is determined more by the shape of the response surface itself rather than by the optimisation methods used. It is also demonstrated that both multivariate normal distribution sampling and Markov chain sampling (the Metropolis algorithm) are more efficient in constructing the proposed optimum parameter curve than the method of uniform random sampling initially investigated for this purpose.