A generic framework for regression regionalization in ungauged catchments

Regionalization of rainfall-runoff models is required for many catchments, where a suitable flow record is not available to enable traditional calibration methods to be used. Most recently, donor catchment approaches have been identified as the most successful at providing suitable model parameter values. However, this approach is less attractive for regions where the number of suitable catchments available to derive model parameters is low. In this case, regression approaches that consider catchment characteristics available in GIS databases may be more appropriate. Approaches such as this have been criticized due to issues associated with the ability to identify suitable parameter values, as well as the approach used to predict them from catchment information, incorporating interactions between parameters. This study proposes a generic framework to enable systematic regression regionalization for a data poor region, considering identification of model parameters using a multi-objective approach, and sensitivity analysis including consideration of parameter interactions. The approach developed has been applied to both lumped and distributed models, in order to investigate the benefits of adopting distributed models to represent catchment heterogeneity. The results indicate that a suitable regression approach can be developed for the region considered, outperforming directly calibrated parameters on a validation period, due to more accurate representation of the recharge process. However, no benefit was found for applying the approach on a distributed scale, most likely due to scale issues with the parameter values.