A nonparametric kernel estimation model is proposed and developed for estimating low flow quantiles. Based on annual minimum low flow data and Monte Carlo simulation tests, the descriptive and predictive ability of the proposed model is compared with that of Weibull models. The results indicate that it has small bias and root mean square error in low flow quantile estimates. Application of the model to data from the Blue Nile has shown that the nonparametric approach is a viable alternative to the Weibull models.