Stu Hamilton, of the Hydrology Corner Blog, comments that a recommended read for anyone interested in uncertainty is Keith Beven (2015) Facets of Uncertainty - Epistemic Uncertainty, Non-stationarity, Likelihood, Hypothesis Testing, and Communication, a paper appearing the Hydrological Sciences Journal, DOI - 10.1080/02626667.2015.1031761.
There are many points that Keith Beven makes that Hamilton strongly agrees with, but he focuses on one question Beven uses as a section heading - Can we talk of confidence rather than uncertainty in model simulations? Keith Beven asks about model simulations, but Hamilton would ask the same thing about hydrometric or even other geological data. Beven states - Decision and policy makers are, however, far more interested in evidence than uncertainty.
Uncertainty in our measurements and the quality of our measurements are both important. They are not different measures of the same thing  they are different things. This seems like a good time to discuss the difference.
1. Data uncertainty is a metric for deviation of the result of measurement from the truth.
2. Data quality is a metric for deviation in the actual process of creating a measurement result from a known standard process that is known to be reliably truthful.
The simple truth is that the actual truth of hydrological variability is unknowable at a high level of precision. In other words, the true uncertainty of any estimate of uncertainty for a value is uncertain because the truth cannot be discovered with certainty. In contrast, the truthfulness of whether a standard was followed, or not, is testable and therefore knowable with very high confidence. If years of experience demonstrate that the standard operating procedure is reliably truthful then proof of compliance to this standard infers that the results of the process are truthful.
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