RiverFlowUA—a package to estimate total uncertainty in ADCP discharge measurements by FOTSE—with an application in hydrometry

Stage measurement errors are generally overlooked when streamflow time series are derived from uncertain rating curves. We introduce an original method for propagating stage uncertainties due to two types of stage measurement errors: (i) errors of the stage read during the gauging and (ii) systematic and nonsystematic (independent) errors of the recorded stage time series. The error models are generic and can be used for any probabilistic rating curve estimation method that provides an ensemble of rating curves. The new method is applied to a range of six contrasting hydrometric stations in France. Uncertainty budgets quantifying the contribution of various error sources to the total streamflow uncertainty are computed and compared for streamflow time series averaged at time intervals from hour to year. A sensitivity analysis is conducted on the stage time series error model to identify the most sensitive parameters. The results are site specific, which illustrates the key role played by the properties of both the hydrometric site and the gauged catchment. Across the range of sites, stage errors of the gaugings are found to have limited impact on rating curve uncertainty, at least for gaugings performed in fair conditions. Nonsystematic errors in the stage time series have a negligible effect, generally. However, systematic stage errors should not be neglected. Over the six hydrometric stations in this study, the 95% uncertainty component reflecting stage systematic errors (from ±0.5 cm to ±6.8 cm) alone ranged from 4% to 12% of daily average streamflow, and from 1% to 3% of yearly average streamflow as sensors were assumed to be recalibrated every 30 days. Perspectives for improving and validating the streamflow uncertainty estimation techniques are eventually discussed.

Section: Discussionmentioning

confidence: 99%

Impact of Stage Measurement Errors on Streamflow Uncertainty

Horner¹,

Renard²,

Coz³

et al. 2018

“…The uncertainty estimates from repeated measures experiments could be compared with those provided by uncertainty propagation methods (González‐Castro et al, ; Mueller, ; Moore et al, ; Muste et al, ; Pierrefeu et al, ). The discrepancy between repeated measures experiments and propagation methods may help better estimate the elemental uncertainty component estimates in the latter methods (Despax et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Uncertainty computation methods were recently developed to estimate the uncertainty of ADCP measurements in stationary (Huang, 2011;Lee et al, 2014) or moving-boat deployment modes: OURSIN (Pierrefeu et al, 2017), QRev (Mueller, 2016), QUant (Moore et al, 2017), or RiverFlowUA (González-Castro et al, 2016). These propagation methods are based on the Guide to the expression of Uncertainty in Measurement (JCGM, 2008a(JCGM, , 2008b.…”

Section: Uncertainty Propagation Methodsmentioning

confidence: 99%

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

Despax

Coz

Hauet

et al. 2019

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.

“…The general application of the GUM uncertainty propagation method to ADCP discharge measurements has been proposed by Muste et al (2004), González-Castro and Muste (2007), Kim and Yu (2010), and the HUG (ISO, 2007). Some tools have been developed for estimating the uncertainty of ADCP measurements in stationary (Huang, 2011;Lee et al, 2014) or moving-boat deployment modes: RiverFlowUA (González-Castro et al, 2016), QUant (Moore et al, 2016), the uncertainty computation originally implemented in QRev (Mueller, 2016(Mueller, , 2020), which will be named QRev-UA in this article, and the original version of OURSIN (Naudet et al, 2019), which has been further modified as described in this article. Most of the tools were developed by hydrometric agencies.…”

Section: Propagation Methods For Estimating the Uncertaintymentioning

confidence: 99%

Validation of an Uncertainty Propagation Method for Moving‐Boat Acoustic Doppler Current Profiler Discharge Measurements

Despax

Coz

Mueller³

et al. 2023

Introduction Moving-Boat ADCP UncertaintyAcoustic Doppler Current Profilers (ADCPs) are now among the most-used instruments for measuring discharge in rivers throughout the world (Boldt & Oberg, 2015;. The technology and general guidance for making ADCP discharge measurements are presented in various manuals and guides, for example, those established by the U.S. Geological Survey (USGS) (Mueller et al., 2013) or the WMO (2010). The ADCP is mounted on a boat or on a small float that transects a river cross-section. The field procedure can be a stationary deployment (section by section) or a moving-boat deployment, wherein each river crossing provides an individual discharge measurement called a transect. This study deals with moving-boat ADCP discharge measurements. The ADCP uses the transit-time of sound waves and the Doppler shift to measure water velocity and depth. Due to physical limitations of the instrument, velocities are measured throughout a limited portion of the cross-section (Mueller et al., 2013). The measured area is decomposed vertically into cells (or bins) that are distributed horizontally into n ensembles (see Figure 1). The discharge Q i,j through a cell is computed from the vector product of the boat velocity 𝐴𝐴 ⃖⃖ ⃗ 𝑣𝑣𝑖𝑖 and the water velocity 𝐴𝐴 ⃖⃖⃖⃖⃖ ⃗ 𝑤𝑤𝑖𝑖𝑖𝑖𝑖 relative to the ADCP system as:where 𝐴𝐴 ⃖ ⃗ 𝑘𝑘 is the unit vertical vector, dz i,j is the cell height, and dt i is the time interval between ensembles.In the unmeasured areas (see Figure 1), discharge has to be estimated (Mueller, 2016). The missing or invalid cells and ensembles are interpolated based on contiguous valid data. The discharge is extrapolated near the riverbed (bottom discharge), near the water surface (top discharge), and near the banks (right and left discharges). Thus, the total discharge Q k in each transect k is a sum of partial discharges: the measured discharge (the sum of individual cell discharges Q i,j ), the interpolated discharges used to fill missing cells and ensembles, respectively