Data errors and hydrological modelling: The role of model structure to propagate observation uncertainty

Observation uncertainty is nowadays recognized as a serious issue undermining the reliability of hydrological studies. For instance, many recent contributions show that river flow observations are affected by errors that may reach 25% even when state-of-the-art measurement techniques are adopted. Yet, there is still little guidance by the literature on the most appropriate modelling strategies to be adopted under observation uncertainty. We carried out a series of simulation experiments and explored how the selection of appropriate model complexity can help reduce the impact of observation uncertainty. We found that model structure plays a relevant role and, in particular, a description of the relevant physical processes that come into play can effectively contribute to limit the impact of data errors and therefore significantly reduce overall uncertainty.