A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations. Issue 7 (19th July 2015) - Coxon, G.; Freer, J.; Westerberg, I. K.; Wagener, T.; Woods, R.; Smith, P. J.
Abstract: Benchmarking the quality of river discharge data and understanding its information content for hydrological analyses is an important task for hydrologic science. There is a wide variety of techniques to assess discharge uncertainty. However, few studies have developed generalized approaches to quantify discharge uncertainty. This study presents a generalized framework for estimating discharge uncertainty at many gauging stations with different errors in the stage‐discharge relationship. The methodology utilizes a nonparametric LOWESS regression within a novel framework that accounts for uncertainty in the stage‐discharge measurements, scatter in the stage‐discharge data and multisection rating curves. The framework was applied to 500 gauging stations in England and Wales and we evaluated the magnitude of discharge uncertainty at low, mean and high flow points on the rating curve. The framework was shown to be robust, versatile and able to capture place‐specific uncertainties for a number of different examples. Our study revealed a wide range of discharge uncertainties (10–397% discharge uncertainty interval widths), but the majority of the gauging stations (over 80%) had mean and high flow uncertainty intervals of less than 40%. We identified some regional differences in the stage‐discharge relationships, however the results show that local conditions dominated in determining the magnitude of discharge uncertainty at a gauging station. This highlights the importance of estimating discharge uncertainty for each gauging station prior to using those data in hydrological analyses. Key Points: A generalized framework for discharge uncertainty estimation is presented Allows estimation of place‐specific discharge uncertainties for many catchments Local conditions dominate in determining discharge uncertainty magnitudes