Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: A Bayesian approach

Abstract

Discharge time series in rivers and streams are usually based on simple stage-discharge relations calibrated using a set of direct stage-discharge measurements called gaugings. Bayesian inference recently emerged as a most promising framework to build such hydrometric rating curves accurately and to estimate the associated uncertainty. In addition to providing the rigorous statistical framework necessary to uncertainty analysis, the main advantage of the Bayesian analysis of rating curves arises from the quantitative assessment of (i) the hydraulic controls that govern the stage-discharge relation, and of (ii) the individual uncertainties of available gaugings, which often differ according to the discharge measurement procedure and the flow conditions. In this paper, we introduce the BaRatin method for the Bayesian analysis of stationary rating curves and we apply it to three typical cases of hydrometric stations with contrasted flow conditions and variable abundance of hydraulic knowledge and gauging data. The results exemplify that the thorough analysis of hydraulic controls and the quantification of gauging uncertainties are required to obtain reliable and physically sound results.

Water and sediment fluxes in Mediterranean mountainous regions: comprehensive dataset for hydro-sedimentological analyses and modelling in a mesoscale catchment (River Isábena, NE Spain)

“…The respective metering data are included in the database. Associated measurement uncertainties were estimated based on recommendations from multiple sources (Le Coz et al, 2014;Pelletier, 1988;Westerberg et al, 2011; see the Supplement for details). Table 2 lists the number of discharge meterings available for each river gauge.…”

“…For the catchment outlet, water level is converted into discharge by means of the water stage-discharge rating curve developed by the authors (for more information, see López-Tarazón et al, 2010), which included the effect of the high suspended sediment concentrations that usually flow through the cross section (hence likely decreasing turbulence, thus providing a higher discharge at the same water level in comparison to clean water). For the sub-catchments, these meterings were used to construct rating curves using Bayesian curve fitting (BaratinAGE, version 2.1, Le Coz et al, 2014) and to derive the time series of discharge from the water stage series (see Sect. 3.2.1).…”

Water and sediment fluxes in Mediterranean mountainous regions: Comprehensive dataset for hydro-sedimentological analyses and modelling in a mesoscale catchment (River Isábena, NE Spain)

“…The respective metering data are included in the database. Associated measurement uncertainties were estimated based on recommendations from multiple sources (Le Coz et al, 2014;Pelletier, 1988;Westerberg et al, 2011).…”

“…For the sub-catchments, these meterings were used to construct rating curves using the Bayesian curve fitting (BaratinAGE, vers. 2.1, Le Coz et al, 2014) and to derive the time series of discharge from 10 the water stage series (see 3.2.1). This procedure additionally yielded the associated 95%-confidence interval CI (for water stage and the parametric uncertainty).…”

Consistency assessment of rating curve data in various locations using Bidirectional Reach (BReach)

“…For all other analyzed stations (except from the station of Clog-y-Fran, UK), a segmented rating curve with two segments is used (e.g., Reitan and Petersen-Øverleir, 2008;Le Coz et al, 2014;McMillan and Westerberg, 2015):…”