Improving Hydrological Model Simulations with Combined Multi-Input and Multimodel Averaging Frameworks

AbstractIt is well known that multimodel averaging can considerably improve hydrological model simulation skill. However, the need to set up and run different models can be a time-consuming task. This work expands on the classic multimodel averaging approach by feeding models with different climate datasets and treating each version as a unique model in the ensemble. Three hydrological models and four climate datasets were combined to produce multimodel/multi-input, multimodel/monoinput and monomodel/multi-input combined flows using a weighting scheme that minimizes the root mean square error (RMSE) between the combined and observed hydrographs. The results show that model averaging improves performance significantly and that the proposed multi-input averaging provides higher skill than multimodel averaging. A combination of all models run with all datasets (12 members in total) produced the best results with the averaged hydrograph being more accurate than any single member on 70% of the catchments. The median Nash-Sutcliffe Efficiency (NSE) metric value increase was 0.07 overall in validation under the multimodel/multi-input framework, all while providing improved simulation reliability.