An incremental rating curve error model is proposed to describe the systematic error introduced when a rating curve is extended by methods such as slope-conveyance, log-log extrapolation, or fitting to indirect discharge estimates. Extension can introduce a systematic or highly correlated error which is anchored by the more extensively measured part of the rating curve. A likelihood function is developed which explicitly accounts for such error and accepts both gauged and binomial-censored data. A sampling experiment based on the three-parameter generalized extreme value distribution was conducted to assess the performance of maximum likelihood quantile estimators. This experiment revealed that substantial, and in some cases massive, degradation in the performance of quantile estimators can occur in the presence of correlated rating curve error (rating error). Comparison of maximum likelihood estimators allowing for and ignoring rating error produced mixed results. As rating error impact and/or information content increased, estimators allowing for rating error tended to perform better, and in some cases significantly better, than estimators ignoring rating error. It is also shown that in the presence of rating error, the likelihood surface may have multiple optima that may result in nonunique solutions for hill-climbing search methods. Moreover, in the presence of multiple optima and constraints on parameters, the likelihood surface may be poorly described by asymptotic approximations. ¿ American Geophysical Union 1996