Estimating flood quantiles at ungauged sites using nonparametric regression methods with spatial components.

Prediction of flood quantiles at ungauged sites is investigated using several nonparametric regression methods including: local regression and generalized additive models (GAM). These methods are used to describe the relationship between runoff variables and catchment descriptors to predict flood quantiles. Previous work reported the presence of spatial correlation in the residuals for these models. To this end, this study proposes and investigates ways of incorporating spatial components. An L-moments regression technique (LRT) is developed to predict L-moments of target sites and flood quantiles are derived by aggregating quantiles from multiple candidate distributions. The predictive power of the proposed methods is evaluated on a large database of Canadian rivers using cross-validation. The results are examined inside different hydrological regions to assess the behaviour of the methods. The results show that GAM and local regression using, respectively, thin plate spline and kriging provide the best predictive powers among the considered methods. [ABSTRACT FROM AUTHOR]