Calibration of a rainfall–runoff model at regional scale by optimising river discharge statistics: Performance analysis for the average/low flow regime

Traditional procedures for rainfall–runoff model calibration are generally based on the fit of individual values of simulated and observed hydrographs. We use here an alternative option that is carried out by matching, in the optimisation process, a set of streamflow statistics. Such an approach has the significant advantage to enable also a straightforward regional calibration of model parameters, based on the regionalisation of the selected statistics. The minimisation of the set of objective functions is carried out by using the AMALGAM algorithm, leading to the identification of behavioural parameter sets. The procedure is applied to a set of river basins located in central Italy: the basins are treated alternatively as gauged and ungauged and, as a term of comparison, the results obtained with a traditional time-domain calibration are also presented. With respect to previous applications of analogous procedures, we investigate here the identification of the target statistics depending on the purposes of the application, and in particular when the focus is on the reproduction of the low-flows. The results show that a suitable choice of the statistics to be optimised leads to interesting results in real world case studies as far as the reproduction of the different flow regimes is concerned.

► Rainfall–runoff model calibration by matching a set of streamflow statistics.
► Regional calibration possible through the regionalisation of the selected statistics.
► Minimisation of the set of OFs by using the multiobjective AMALGAM algorithm.
► Choice of the target statistics for the reproduction of low-flows.
► Match of low-flow indexes important for simulating low to average runoff variability.