Proof of concept for Bayesian inference of dynamic rating curve uncertainty in a sparsely gauged watershed.

Hydrometric data poverty compounds the challenge of accounting for uncertainties in non-stationary stage–discharge relationships. This paper builds on three methods to explore the integration of a dynamic approach to rating curve assessment and a physically based Bayesian framework for quantifying discharge amid geomorphologically induced rating shifts in a sparsely gauged alluvial river. The Modified GesDyn–FlowAM–BaRatin method entails sequentially segmenting gaugings according to residual indicators of riverbed instability and channel conveyance variability, leveraging cross-sectional surveys to augment calibration data, and eliciting hydraulic priors for probabilistic rating curve estimation. This method is applied to a Philippine watershed, where quarrying near the gauging station has ostensibly caused morphodynamic adjustments. Time-variable credible intervals for discharge are computed. The optimal estimates root mean square error (RMSE = 2.96 m3/s) from maximum a posteriori rating curves outperform the hydrographer's benchmark (RMSE = 5.00 m3/s), whose systematic errors from the gauged flows arise from lapses in shift detection. [ABSTRACT FROM AUTHOR]