This thesis proposes an approach for tackling the problem of data-shortage in hydrological modelling. A hydrological model, in the context of this thesis, translates meteorological data to stream-flow of a river, for a specific catchment. A model incorporates parameters that describe the dynamics of a chosen model, in this case a FLEX (Flux-Exchange) based model. All parameter together form a set. These parameters are unknown and therefore need to be determined, which is done via a calibration process. In the calibration process, the modelled flow will be evaluated against the observed stream-flow over a period of time for a certain hydrological signature. A hydrological signature is defined as the quantification of specific information concerning the rainfall-runoff dynamics of a catchment, e.g. the mean stream-flow. The evaluation for a certain hydrological signature is known as an evaluation criterion. Literature deems the use of one or two criteria in combination with multiple years of data enough to ensure a good performance from the parameter-sets that exit the calibration process. As this amount of data is not available everywhere, this thesis proposes to still ensure good model performance for less observed data. For this purpose, a selection was made of evaluation criteria to be applied in the calibration process. These criteria were selected in such a way that various different characteristics of the hydrological response were covered. A benchmark was created for comparison purposes in which 10 years of data was used during calibration and one evaluation criterion. After the benchmark, the observed data was shortened and evaluation criteria were added. The results showed that 6 months worth of data in combination with all criteria would create benchmark-level performances without extra information. The same level of performance can be reached with 3 months of data but this would require extra information. This information would be e.g. during which period of time observed data should be collected.