Document Type : Research Paper
Authors
1 Gonbad Kavous University
2 University of Tehran
Abstract
Any modeling and decision-making process in watershed management fundamentally depends on accurate discharge data at the basin outlet. However, direct measurement of discharge is both costly and time-consuming. Therefore, at hydrometric stations, water stage is routinely recorded, and discharge is estimated using the rating-curve equation corresponding to the observed stage. Nevertheless, several sources of uncertainty, including errors in measuring flow velocity, cross-sectional area, and stage height, as well as model limitations in estimating extreme flows and temporal variations in channel morphology caused by erosion, sediment deposition, and vegetation growth, result in inaccuracies in rating-curve-based discharge estimations. Such uncertainties propagate through hydrological model outputs and management decisions, potentially leading to considerable economic and environmental losses. Consequently, it is essential to quantify the uncertainty bounds of rating-curve-derived discharge estimates in order to mitigate risks associated with measurement and estimation errors. To date, both classical statistical and Bayesian approaches have been employed for uncertainty estimation in rating curves. The Bayesian framework, in particular, offers significant advantages: in addition to incorporating observational data through the likelihood function, it allows prior hydraulic knowledge of the station to be embedded in the model through prior distributions. With recent advances in computational power and the widespread application of Markov Chain Monte Carlo (MCMC) sampling techniques, Bayesian methods have become a powerful and flexible tool for rating-curve uncertainty estimation, and various model structures have been introduced in recent years. Accordingly, the present study estimates rating-curve uncertainty using a Bayesian approach for three hydrometric stations located in Golestan Province.
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