Insufficient temporal monitoring of water quality in streams or engineered drains alters the apparent shape of storm chemographs, resulting in shifted model parameterisations and changed interpretations of solute sources that have produced episodes of poor water quality. This so-called 'aliasing' phenomenon is poorly recognised in water research. Using advances in in-situ sensor technology it is now possible to monitor sufficiently frequently to avoid the onset of aliasing. A systems modelling procedure is presented allowing objective identification of sampling rates needed to avoid aliasing within strongly rainfall-driven chemical dynamics. In this study aliasing of storm chemograph shapes was quantified by changes in the time constant parameter (TC) of transfer functions. As a proportion of the original TC, the onset of aliasing varied between watersheds, ranging from 3.9-7.7 to 54-79 %TC (or 110-160 to 300-600 minutes). However, a minimum monitoring rate could be identified for all datasets if the modelling results were presented in the form of a new statistic, ΔTC. For the eight H⁺, DOC and NO₃-N datasets examined from a range of watershed settings, an empirically derived threshold of 1.3(ΔTC) could be used to quantify minimum monitoring rates within sampling protocols to avoid artefacts in subsequent data analysis.