Reliability and uncertainty in flow measurement techniques - some current thinking

Whalley, N.; Iredale, R. S.; Clare, A.F.

doi:10.1016/s1464-1917(01)95019-6

Cited by 10 publications

(9 citation statements)

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“…This is problematic in the case of floodstage gaugings which are rarely collected. The process relies on expert judgement to determine the frequency and extent to which the curve should be updated (Whalley et al, 2001), and implicitly on an assessment of the balance between gauging errors and rating-curve change. The Wairau therefore presents a good example of a catchment where the assumption of zero uncertainty in the rating curve is unjustified and a rainfall-runoff model calibration technique that is able to account for rating-curve estimation errors would be a valuable tool.…”

Section: Catchmentmentioning

confidence: 99%

“…Hence, we set the standard deviation D 0Ð04Q True . It is preferable to set the variance according to site-based knowledge, as here; however, alternatively, standard values could be used such as those provided by Pelletier (1988) or Whalley et al (2001) which are comparable with the value used here. The distribution is truncated at 3 (12%) error, which captures >99% of the distribution, while avoiding very large error values which are not considered reasonable (the 12% bound only represents possible error for a single gauging and does not include error due to rating curve interpolation/extrapolation or cross-section change).…”

Section: Uncertainty In Gauging Measurement and Rating Curve Formmentioning

confidence: 99%

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Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions

McMillan

Freer

Pappenberger

et al. 2010

Hydrological Processes

235

217

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10.1002/hyp.7587.abs In order to quantify total error affecting hydrological models and predictions, we must explicitly recognize errors in input data, model structure, model parameters and validation data. This paper tackles the last of these: errors in discharge measurements used to calibrate a rainfall-runoff model, caused by stage–discharge rating-curve uncertainty. This uncertainty may be due to several combined sources, including errors in stage and velocity measurements during individual gaugings, assumptions regarding a particular form of stage–discharge relationship, extrapolation of the stage–discharge relationship beyond the maximum gauging, and cross-section change due to vegetation growth and/or bed movement. A methodology is presented to systematically assess and quantify the uncertainty in discharge measurements due to all of these sources. For a given stage measurement, a complete PDF of true discharge is estimated. Consequently, new model calibration techniques can be introduced to explicitly account for the discharge error distribution. The method is demonstrated for a gravel-bed river in New Zealand, where all the above uncertainty sources can be identified, including significant uncertainty in cross-section form due to scour and re-deposition of sediment. Results show that rigorous consideration of uncertainty in flow data results in significant improvement of the model's ability to predict the observed flow. Copyright © 2010 John Wiley & Sons, Ltd

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Section: Catchmentmentioning

confidence: 99%

Section: Uncertainty In Gauging Measurement and Rating Curve Formmentioning

confidence: 99%

Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions

McMillan

Freer

Pappenberger

et al. 2010

Hydrological Processes

235

217

View full text Add to dashboard Cite

show abstract

“…It has also been shown (Whalley et al 2001) that at very low speeds, the performance of current meters may vary greatly, often with significant deviations. In these cases, increasing the number of measured vertical points or the number of points along the same vertical line can often improve the measurements.…”

Section: Use Of Current Metersmentioning

confidence: 99%

Experimental methods for river discharge measurements: comparison among tracers and current meter

Tazioli

2011

Hydrological Sciences Journal

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“…In order to develop a likelihood function for the model in Equation (10), one has to specify the error associated with the discharge measurements Q 1 , Ð Ð Ð , Q n . This can be achieved by considering the following two points: (1) the accuracy of a measurement made with current metre, dilution techniques, acoustic instruments, etc., is almost without exception given in percentage of the true or measured discharge and (2) the error of a discharge measurement is typically much smaller than the measured discharge (Day, 1976;Pelletier, 1988;Whalley et al, 2001;Herschy, 2002). Together with the fact that a measured discharge is always positive, it is thus indicated that a lognormally distributed multiplicative measurement error with unit expectancy and constant variance can be considered for the whole dataset.…”

Section: Basic Regression Model Developmentmentioning

confidence: 99%

Bayesian analysis of stage–fall–discharge models for gauging stations affected by variable backwater

Petersen‐Øverleir

Reitan

2009

Hydrological Processes

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Abstract:Parsimonious stage-fall-discharge rating curve models for gauging stations subject to backwater complications are developed from simple hydraulic theory. The rating curve models are compounded in order to allow for possible shifts in the hydraulics when variable backwater becomes effective. The models provide a prior scientific understanding through the relationship between the rating curve parameters and the hydraulic properties of the channel section under study. This characteristic enables prior distributions for the rating curve parameters to be easily elicited according to site-specific information and the magnitude of well-known hydraulic quantities. Posterior results from three Norwegian and one American twin-gauge stations affected by variable backwater are obtained using Markov chain Monte Carlo simulation techniques. The case studies demonstrate that the proposed Bayesian rating curve assessment is appropriate for developing rating procedures for gauging stations that are subject to variable backwater.

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Reliability and uncertainty in flow measurement techniques - some current thinking

Cited by 10 publications

References 0 publications

Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions

Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions

Experimental methods for river discharge measurements: comparison among tracers and current meter

Bayesian analysis of stage–fall–discharge models for gauging stations affected by variable backwater

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