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. (C) 2012 Elsevier Ltd. All rights reserved.