While the application of uncertainty propagation methods to hydrometry is still challenging, in situ collaborative interlaboratory experiments are a valuable tool for empirically estimating the uncertainty of streamgauging techniques in given measurement conditions. We propose a simple procedure for organizing such experiments and processing the results according to the authoritative ISO standards related to interlaboratory experiments, which are of common practice in many metrological fields. Beyond the computation and interpretation of the results, some issues are discussed as regards: the estimation of the streamgauging technique bias in the absence of accurate enough discharge references in rivers; the uncertainty of the uncertainty estimates, according to the number of participants and repeated measurements; the criteria related to error sources which are possibly meaningful for categorizing measurement conditions. The interest and limitations of the in situ collaborative interlaboratory experiments are exemplified by an application to the
Repeated measures experiments can be conducted to empirically estimate the uncertainty of a stream gauging method, such as the widespread moving‐boat acoustic Doppler current profilers (ADCPs) approach. Previous ADCP repeated measures experiments, also known as interlaboratory comparisons, provided a credible range of uncertainty estimates reflecting the quality of the site conditions. However, the method, which is a one‐way analysis of variance, only addresses the uncertainty of one lumped factor that combines several distinct factors: instrument, operator, procedure, and cross‐section effects. To decompose the uncertainty of ADCP streamflow measurements due to cross‐section selection and team effects, a large repeated measures experiment has been conducted in the Taurion River (France). The experiment design was crossed and balanced, with two sets of 24 teams circulated over two sets of 12 cross sections. A constant flow rate was released from a dam, located immediately upstream of the experimental site. Prior to the statistical analysis, a data quality review was performed using the U.S. Geological Survey QRev software to clean the data set from avoidable errors and to homogenize the discharge computations. A two‐way analysis of variance was applied to quantify the cross‐section effect, the team effect, and their interaction, which was found to dominate the pure cross‐section effect. It was then possible to predict the average uncertainty of multiple‐transect ADCP discharge measurements, depending on the number of teams, cross sections, and repeated transects included in the discharge average. The method opens interesting avenues for documenting difficult‐to‐estimate uncertainty sources of stream gauging techniques in other measuring conditions, especially the most adverse ones.
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