Decomposition of Uncertainty Sources in Acoustic Doppler Current Profiler Streamflow Measurements Using Repeated Measures Experiments

Abstract

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.

Adaptively monitoring streamflow using a stereo computer vision system

“…Results highlight the advancements in stereophotogrammetry and machine learning approaches can overcome some of the challenges of non-contact optical stream gauging and provide insights into the added value of these emerging operational technologies. Whilst a significant uncertainty on the order of 6% has been found to be possible from user variations in ADCP discharge estimations (Despax et al, 2019) as well as comparable deviations evident in a study of ADCP measurement validation (Oberg and Mueller, 2007), a similar order of magnitude in difference between direct technology comparisons was found in all-but-one exceptional case in an irrigation channel, NSW, Australia. A study in an irrigation canal using an experimental video camera system with LSPIV and optical water level detection on a staff gauge demonstrated results within 5% of the reference estimates, but highlighted that reflections and shadows produced There has been some LSPIV discharge uncertainty estimations undertaken from recordings of a stream in the French Alps (Dramais et al, 2011) showing less than 2% discharge deviation for recording durations more than 4 seconds, consistent with the CVSG results tested using different durations.…”

Dynamic Optimization Method for Broadband ADCP Waveform with Environment Constraints

“…By substituting Equation ( 17) into ( 16), the cost function σ v is transformed into a single variable function: (18) Equation (19) shows that the cost function and the number of repeats are non-linear. In addition, when N is fixed, the larger ∆z, the higher the accuracy of velocity estimation, so ∆z takes the maximum allowable layer thickness ∆z m .…”

“…In many studies, the measurement uncertainty guidelines [ 15 ], Monte Carlo simulation [ 16 ] and American Aerospace Society [ 17 ] methods were used to analyze the uncertainty of various types of ADCP measurements strictly. Aurélien Despax et al studied the uncertainty of ADCP discharge measurement caused by cross-section selection and human operation by using repeated measures experiments and put forward some strategies to reduce the measurement uncertainty [ 18 ]. ADCP is usually installed on a moving carrier to complete the measurement.…”

Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives

“…Streamflow monitoring is an essential part of hydrology, contributing to water resources policy and management, hydraulic construction design, and development and improvement of models (Blöschl et al., 2019; Manfreda et al., 2018). The standard monitoring framework for streamflow monitoring makes the use of flow meters or acoustic Doppler current profiles (ADCPs; Bjerklie et al., 2020; Despax et al., 2019; Yorke & Oberg, 2002). Although these devices are currently considered the gold standard for streamflow measurements, they have many drawbacks.…”