Sharing perceptual models of uncertainty – on the use of soft information about discharge data uncertainty

Many hydrologists face the situation that they have no, or very limited, information about the uncertainty in the discharge data they are using. Data uncertainty is rarely communicated by monitoring agencies and data providers – and is often not available on request. This means that data users typically treat data as if they are error-free, whereas in reality there can be large uncertainties and errors.

However, the absence of metadata and ‘hard’ information about data uncertainty does not mean that there is no information about the data uncertainty. Instead, we can use other types of ‘soft’ information to understand the likelihood that discharge data in a particular location are uncertain. For example, if high flows are of short duration (i.e., a few hours) and the rainfall-runoff lag time is short, it is practically quite difficult to manage to gauge high flows, leading to likely extrapolation of stage–discharge rating curves and large high flow uncertainty. A second example is if a river is ice-covered during the winter season, then most of the winter water-level time series is subjectively estimated, leading to substantial uncertainty in winter low flows. Such soft information about data uncertainty is well known by field hydrologists and data uncertainty experts but is not as commonly known in the wider hydrological community. In this presentation we focus on uncertainty in discharge data calculated from stage–discharge rating curves and aim to share – and to encourage sharing – of soft information about data uncertainty sources, to promote more informed decisions on data uncertainty in hydrological studies.

We summarize the soft information about discharge data uncertainty as a perceptual model of uncertainty. Our perceptual model divides the soft information into three categories: station characteristics, climate and flow regime, and catchment characteristics. For each category we present and describe different types of soft information, the uncertainty sources and impacts they can inform us about, and sources for each soft information type (e.g., photos, satellite images, land use). We find that soft information can inform us about three main types of uncertainty sources: uncertainty related to the hydraulic control, uncertainty related to incomplete gauging of the full flow range, and uncertainty due to measurement error.

Our generalised perceptual model can be seen as a smorgasbord of information about uncertainty sources, where the soft information can be considered as relevant to a particular dataset and can inform us if high or low data uncertainty is likely. We believe that a key benefit of the type of generalized perceptual model of uncertainty we present is to facilitate dialogue on, and understanding of, possible sources of observational uncertainties and their impacts.  We encourage others to complement our perceptual model of discharge data uncertainty based on experience from different regions and for other discharge monitoring techniques such as index-velocity stations or drone/camera-based methods.