Can we use soft information to estimate discharge data uncertainty for large samples of catchments?

Understanding how data uncertainties impact on our analyses is essential to draw the right conclusions about hydrological processes and their change in space and time. However, understanding the impact of data uncertainties is challenging when working with large numbers of catchments: data uncertainty estimates are rarely available from data providers and producing such estimates requires substantial efforts.

In the absence of such ‘hard’ information about data uncertainty, Westerberg and Karlsen (2024), suggested using ‘soft’ information to qualitatively estimate discharge data uncertainty and to summarise the soft information in a generalized perceptual model of uncertainty. They use three categories of soft information: station characteristics, climate and flow regime, and catchment characteristics. An example of soft information about the flow regime is that there are rare extreme high flows, this impedes high flow gauging and therefore increases high flow uncertainty. Another example of a climate characteristic is the presence of river ice-cover that increases low flow uncertainty.

In this study, we take the generalized perceptual model of discharge data uncertainty from Westerberg and Karlsen and translate it into catchment and station metadata from the Camels-GB dataset (Coxon et al., 2020) and the UK National River Flow Archive (https://nrfa.ceh.ac.uk). This enables us to evaluate how useful soft information is to identify stations with low and high data uncertainty by comparing it to the ‘hard’ uncertainty estimates at hourly and daily time scales from previous detailed stage–discharge rating curve uncertainty analyses (Coxon et al., 2015; Westerberg et al., 2016). We explore which metadata are most useful as soft information about high and low flow uncertainty respectively and recommend useful metadata on discharge data uncertainty to be included in large sample datasets.

 

Coxon, G., J. Freer, I. K. Westerberg, T. Wagener, R. Woods, and P. J. Smith (2015), A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations, Water Resour. Res., 51, 5531–5546, doi:10.1002/2014WR016532.

Westerberg, I. K., T. Wagener, G. Coxon, H. K. McMillan, A. Castellarin, A. Montanari, and J. Freer (2016), Uncertainty in hydrological signatures for gauged and ungauged catchments, Water Resour. Res., 52, 1847–1865, doi:10.1002/2015WR017635.

Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R. (2020): CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020.

Westerberg, I. K., & Karlsen, R. H.  (2024). Sharing perceptual models of uncertainty: On the use of soft information about discharge data. Hydrological Processes, 38(5), e15145. https://doi.org/10.1002/hyp.15145