Observational data is the foundation for most of hydrological science. However, observational data uncertainty can often have high magnitudes (e.g., ±50%–100% typical low flow uncertainty, McMillan et al., 2012) and be of complex character (e.g., Viney & Bates, 2004), which means that in some cases our data may be of limited use or even misleading in our quest to understand hydrological processes (e.g., Kauffeldt et al., 2013). Discussion of the impacts of data uncertainty on process understanding reaches from very early hydrological observations (Heberden, 1769), through early uncertainty estimation techniques (Horton, 1923) and continuing to the plea from Sevruk (1987) that data errors must not be ignored. The impacts of intrinsic data limitations and uncertainties on modelling of hydrological processes have also been long discussed by for example Klemes (1986), Beven (2002), Sivapalan et al. (2003) and Kirchner (2006). Understanding, quantifying and documenting observational uncertainty and their impacts on hydrological analysis and modelling in any study is therefore essential to draw robust conclusions about hydrological processes.