A Comparison of Methods for Streamflow Uncertainty Estimation

International audienceStreamflow time series are commonly derived from stage-discharge rating curves, but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast, uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time-varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates

Conférence invitée : Some challenges in hydrometry

Hydrometry has always been an important branch in hydrology. Even so, the quality of streamflow data might not have evolved in accordance with the advances in hydrological sciences. A too large uncertainty in published data is still a challenge in hydrometry. Hydrometric field work is a diverse profession. Technical equipment and personnel competence are usually in accordance with accepted standards. Hence, there are no technical or practical reasons that should indicate that it is impossible to achieve accurate data for a particular gauging station, given that it is reasonably located in the river channel. This short note argues that two main reasons for a too large uncertainty in published data are too comprehensive hydrometric networks and no criteria for accuracy of published data. It is also argued that the chief uncertainty factor in rating curves is extrapolation uncertainty. A short description of a project in Statkraft that has been launched to minimize extrapolation of rating curves in the network owned by the company is also given.Petersen-Øverleir Asgeir. Conférence invitée : Some challenges in hydrometry. In: 35es journées de l’hydraulique de la Société Hydrotechnique de France. Hydrométrie 2013. Paris, 15-16 mai 2013. 2013

Estimating the discharge rating curve by nonlinear regression - The frequentist approach

This report provides a discussion about the fundamentals of the frequentist approach to the classical nonlinear least squares head - discharge power-law rating curve model, which is a vital procedure in practical hydrology. It is shown that the multivariate minimization problem of the classical nonlinear least squares rating curve model is equivalent to the maximization of a single argument function. We propose four general criteria which the discharge measurements should meet if a trustable frequentist least squares rating curve estimate should exist. The proposed criteria are applied to a large number of real-life discharge measurements, which suggest that the criteria are particularly useful in practice. We also show that the breakdown of one of the criteria implies an exponential law relationship between the head and the discharge. Numerical maximization of the single argument function and inference are discussed

Some challenges in hydrometry

Hydrometry has always been an important branch in hydrology. Even so, the quality of streamflow data might not have evolved in accordance with the advances in hydrological sciences. A too large uncertainty in published data is still a challenge in hydrometry. Hydrometric field work is a diverse profession. Technical equipment and personnel competence are usually in accordance with accepted standards. Hence, there are no technical or practical reasons that should indicate that it is impossible to achieve accurate data for a particular gauging station, given that it is reasonably located in the river channel. This short note argues that two main reasons for a too large uncertainty in published data are too comprehensive hydrometric networks and no criteria for accuracy of published data. It is also argued that the chief uncertainty factor in rating curves is extrapolation uncertainty. A short description of a project in Statkraft that has been launched to minimize extrapolation of rating curves in the network owned by the company is also given

A comparison of methods for streamflow uncertainty estimation

Streamflow time series are commonly derived from stage-discharge rating curves, but theuncertainty of the rating curve and resulting streamflow series are poorly understood. While differentmethods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding ofhow uncertainty estimates differ between methods due to different assumptions and methodologicalchoices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at threeriver locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a widerange of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), fullwidth 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast,uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of therating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (UnitedKingdom), where the hydraulic control is unstable at low flows. Differences between methods result fromdifferences in the sources of uncertainty considered, differences in the handling of the time-varying nature ofrating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptionswhen extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of anuncertainty method requires a match between user requirements and the assumptions made by theuncertainty method. Given the signi ficant differences in uncertainty estimates between methods, we suggestthat a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates