Evaluation of regionalisation methods for predicting runoff in ungauged catchments in southeast Australia

Runoff predictions in ungauged basins or catchments are regarded as one of the most challenging tasks in surface hydrology. To develop practical runoff prediction methods is important for assessing water resources in an ungauged or poorly gauged catchment which is usually located in headwater regions. Regionalisation is typically used for runoff predictions in ungauged catchments. This can be defined as the process of transferring parameter values from a gauged catchment to the target ungauged catchment. This study evaluates three commonly used methods for selecting donor catchments whose entire set of parameter values are used to model runoff in a target ungauged catchment: spatial proximity, physical similarity and integrated similarity. In the physical similarity method, the catchment with the most similar physical characteristics to the target ungauged catchment is chosen as the donor catchment. In the spatial proximity method, the geographically closest gauged catchment to the target ungauged catchment is chosen as the donor catchment. In the integrated similarity method, the spatial proximity and physical similarity measures are considered together to choose the donor catchment. The modelling results are also compared to results from a randomly selected donor catchment. The study also compares results from the use of a single donor catchment versus the output averaging of results from multiple donor catchments. The regionalisation methods are assessed using the conceptual Xinanjiang daily rainfall-runoff model. This model is applied to 210 relatively unimpacted catchments in south-east Australia. To assess the model predictions of daily runoff in 'ungauged' catchments, each of the 210 catchments is left out in turn and considered as an 'ungauged' catchment, and the entire set(s) of parameter values from the donor catchment(s) are used to model runoff in the 'ungauged' catchments. The results show that the biggest benefit comes from an educated selection of donor catchments and output averaging of results from multiple donor catchments. The Nash-Sutcliffe Efficiency (NSE) values from an educated selection of donor catchments are on average about 0.2 higher than the NSE values from a random selection of donor catchments. The output averaging of simulations from multiple donor catchments generally gives better daily runoff simulations than the use of a single donor catchment because it averages out the effect of choosing a poor donor catchment. The NSE values from the output averaging of results from multiple donor catchments are on average about 0.1 higher than the NSE values from the use of a single donor catchment. Compared to the differences between single donor and multiple donor output average and between random selection and an educated selection of donor catchments, the difference between the three commonly used methods for selecting donor catchments is on average relatively small. However, in the poorer modelled catchments, the integrated similarity method gives better results than the spatial proximity method which in turn gives better results than the physical similarity method.