Bayesian Rating Curve Modeling: Alternative Error Model to Improve Low-Flow Uncertainty Estimation

Garcia, Rodrigo; Costa, Veber Afonso Figueiredo; Silva, Francisco

doi:10.1061/(asce)he.1943-5584.0001903

Cited by 8 publications

(10 citation statements)

References 42 publications

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“…Structural uncertainty is reported to stem from both random and epistemic errors (Juston et al, 2014;McMillan & Westerberg, 2015). Due to this fact, incorporating systematic errors in rating curve modeling is still a challenging task (Garcia et al, 2020). Hence, in this paper we assume that epistemic structural errors may be treated as random for inference purposes, although this assumption may aggregate bias to streamflow estimates.…”

Section: Statistical Modelsmentioning

confidence: 99%

“…Rating curves may be affected by both random and epistemic uncertainty. The random component is usually associated with equipment precision and measurement conditions, which might not be precisely known (Le Coz, 2012;Garcia et al, 2020). In fact, backwater effects or hysteresis may entail strong deviations from the reference stage-discharge relationship during a measurement expedient, but the occurrence of these conditions might not be determined a priori (Le Coz et al, 2014;McMillan & Westerberg, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, a great deal of research effort (e.g., Kiang et al, 2018 and references therein) has been dedicated to understanding and quantifying the effects of rating curve uncertainty in hydrological applications. The propagation of rating curve uncertainty in flood frequency analysis (Steinbakk et al, 2016;Osorio & Reis Junior, 2016), rainfall-runoff modeling (Sikorska & Renard, 2017) and flood mapping (Kastali et al, 2021) has also comprised an active research field, and the explicit account of the rating curve errors has improved the definition of flood management strategies (McMillan et al, 2017) and facilitated the communication of the cost-risk tradeoff to decision makers (Garcia et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The Bayesian paradigm has offered suitable alternatives for properly accounting for the rating curve uncertainty and preserving the physical realism of the predicted steamflows in extrapolation conditions (Moyeed & Clarke, 2005;Renard et al, 2010;Reitan & Petersen-Øverleir, 2009;McMillan et al, 2010;Le Coz et al, 2014;Steinbakk et al, 2016;Garcia et al, 2020;Kastali et al, 2021). Bayesian methods allow introducing expert knowledge in inference procedures via informative prior distributions for the rating curve parameters under hydraulic considerations (friction, geometry, hydraulic controls) and, hence, model the rating curve on physical grounds.…”

Section: Introductionmentioning

confidence: 99%

“…Among the available Bayesian methods, this paper has utilized the BaRatin (Bayesian Rating Curve; Le Coz et al, 2014) for modeling the rating curve under a formal probabilistic framework. BaRatin builds a conditional likelihood based on uncertain gaugings (i.e., measurements with errors) and uses hydraulic controls for associating a theoretical flow model for the stage-discharge relationship, which allows well-posed inferences on the upper portion of the rating curve (Le Coz et al, 2014;Osorio & Reis Junior, 2016;Ocio et al, 2017;Sikorska & Renard, 2017;Garcia et al, 2020;Kastali et al, 2021). BaRatin also explicitly accounts for the distinct sources of uncertainty, which allows segregating measurement, sampling and structural errors, and is paramount for decomposing the predictive uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the effects of rating curve uncertainty in flood frequency analysis

Vieira

Sampaio

Costa

et al. 2022

RBRH

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Maximum flows are often estimated from flood frequency analysis, by means of the statistical fitting of a theoretical probability distribution to maximum annual flow data. However, because of the limitations imposed by the practice of at-site flow measurement, empirical models are applied as the rating curve for estimating streamflow. These curves are approximations of the actual flows and incorporate different sources of uncertainty, especially in the extrapolation portions. These uncertainties are propagated in the frequency analysis and influence the estimated quantiles. For better understanding and describing the influence of the stage-discharge uncertainty in this process, the results of Bayesian rating curve modeling, which considers the physical knowledge of the gauging station as prior information, were combined with Bayesian flood frequency analysis under asymptotic extreme value theory. The method was applied to the Acorizal stream gauging station, located in the interior of the state of Mato Grosso - BR. The main results suggested that, although the uncertainties of the rating curve can be relevant in the estimation of maximum flow quantiles, the uncertainties arising from finite-sample inference might exert greater impacts on the flow credibility intervals even for moderate sample sizes.

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Section: Statistical Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessing the effects of rating curve uncertainty in flood frequency analysis

Vieira

Sampaio

Costa

et al. 2022

RBRH

Self Cite

View full text Add to dashboard Cite

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Effect of streamflow measurement error on flood frequency estimation

Velásquez,

Krajewski

2024

Stoch Environ Res Risk Assess

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Significant errors often arise when measuring streamflow during high flows and flood events. Such errors conflated by short records of observations may induce bias in the flood frequency estimates, leading to costly engineering design mistakes. This work illustrates how observational (measurement) errors affect the uncertainty of flood frequency estimation. The study used the Bulletin 17 C (US standard) method to estimate flood frequencies of historical peak flows modified to represent the measurement limitations. To perform the modifications, we explored, via Monte Carlo simulation, four hypothetical scenarios that mimic measurement errors, sample size limitations, and their combination. We used a multiplicative noise from a log-normal distribution to simulate the measurement errors and implemented a bootstrap approach to represent the sampling error. Then, we randomly selected M samples from the total N records of the observed peak flows of four gauging stations in Iowa in central USA. The observed data record ranges between 76 and 119 years for watersheds with drainage areas between 500 and 16,000 km2. According to the results, measurement errors lead to more significant differences than sampling limitations. The scenarios exhibited differences with median magnitudes of up to 50%, with some cases reaching differences up to 100% for return periods above 50 years. The results raise a red flag regarding flood frequency estimation that warrants looking for further research on observational errors.

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