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

Abstract

The estimation of hydrometric rating curves uncertainty has constituted an active topic of research on hydrology. In this regard, the BaRatin inference framework, which estimates rating curves on the basis of prior hydraulic knowledge, has been considered a promising alternative. In building inference setups, a variety of structural error models have been combined with BaRatin. However, most of them neglect the potentially high scatter levels in the lower portion of rating curves, caused by changes in the channel bottom. For addressing this issue, in this paper we propose a Gaussian heteroscedastic structural error model, which attributes larger uncertainty for both upper and lower portions of the rating curve. The inference framework was applied to two catchments in Brazil with distinct hydraulic controls and channel bed stability conditions. Results demonstrated that, under the proposed error model, the total uncertainty intervals encompassed most measured large flows and even relatively high scatters of low discharges, which suggest the overall suitability of the proposed modeling strategy and its capacity to achieve more realistic intervals of uncertainty.