Relative merits of different methods for runoff predictions in ungauged catchments

Abstract

There have been numerous regionalization studies on runoff prediction in ungauged catchments. This study evaluates the relative benefits of different methods using two conceptual daily rainfall‐runoff models, Xinanjiang and SIMHYD, on 210 relatively unimpacted catchments in southeast Australia. The results show that runoff predictions in ungauged catchments can benefit from a smart selection of donor catchments whose optimized parameter values are used to model runoff in the target ungauged catchment, output averaging of results from multiple‐donor catchments and incorporating leaf area index data into the rainfall‐runoff models. The biggest benefit comes from an educated selection of donor catchments (compared to a random selection of donor catchments) and output averaging of results from multiple‐donor catchments. The difference between the three commonly used approaches for selecting donor catchments is relatively small. The spatial proximity approach (where the geographically closest catchment is used as the donor catchment) performs slightly better than the physical similarity approach (where the catchment with the most similar attributes is used as the donor catchment), and the integrated similarity approach, which combines the spatial proximity and physical similarity approaches, performs only very marginally better than the spatial proximity approach. The incorporation of leaf area index data into the rainfall‐runoff models shows marginal improvements to the modeling results, although a more appropriate integration of vegetation and other remotely sensed data may further improve the results.