A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations

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 stagedischarge 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.

Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use

“…Simulated water balance components vary considerably due to various uncertainties of GHMs (Haddeland et al, 2011;Schewe et al, 2014) including human water use, model improvements over time (e.g., see the different results of the Water Global Assessment and Prognosis (WaterGAP) model in Müller , their Table 5), and climate forcing (Biemans et al, 2009;Voisin et al, 2008) as well as uncertainties in discharge observations (Coxon et al, 2015;McMillan et al, 2012). In addition to these uncertainties, water resources estimates differ due to different reference periods (Wisser et al, 2010).…”

Glacio-hydrological melt and runoff modelling: a limits of acceptability framework for model selection

“…Accordingly, McMillan and Westerberg (2015) propose a method 15 to define rating curve uncertainty which accounts for both sources of error and has been used to estimate uncertainty in river discharge signatures (Westerberg et al, 2016). The random error component was defined from analysis of 27 flow gauging stations in the UK with stable ratings and without obvious epistemic errors (Coxon et al, 2015). They conclude that this source of error is best approximated by a logistical distribution model.…”

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

“…When validating model output against observed river discharge, measurement errors should be taken into account, ideally in a station-and discharge-specific manner. None of the 500 UK gauging stations has a discharge observation uncertainty of less than 10 % (for individual measurements at mean flow conditions) due to uncertain stage-discharge relationships, while 83 % of the stations for which uncertainty could be determined has an uncertainty of less than 40 % (Coxon et al 2015).…”